Management Study

How do you define “index numbers”? Narrate the nature and types of index number with adequate example

noreply@blogger.com (Manoj Maikhuri) — Tue, 13 Oct 2015 08:29:00 +0000

Index numbers are meant to study the change in the effects of such factors which cannot be measured directly. According to Bowley, “Index numbers are used to measure the changes in some quantity which we cannot observe directly”. For example, changes in business activity in a country are not capable of direct measurement but it is possible to study relative changes in business activity by studying the variations in the values of some such factors which affect business activity, and which are capable of direct measurement.

Index numbers are commonly used statistical device for measuring the combined fluctuations in a group related variables. If we wish to compare the price level of consumer items today with that prevalent ten years ago, we are not interested in comparing the prices of only one item, but in comparing some sort of average price levels. We may wish to compare the present agricultural production or industrial production with that at the time of independence. Here again, we have to consider all items of production and each item may have undergone a different fractional increase (or even a decrease). How do we obtain a composite measure? This composite measure is provided by index numbers which may be defined as a device for combining the variations that have come in group of related variables over a period of time, with a view to obtain a figure that represents the ‘net’ result of the change in the constitute variables.

Index numbers may be classified in terms of the variables that they are intended to measure. In business, different groups of variables in the measurement of which index number techniques are commonly used are (i) price, (ii) quantity, (iii) value and (iv) business activity. Thus, we have index of wholesale prices, index of consumer prices, index of industrial output, index of value of exports and index of business activity, etc. Here we shall be mainly interested in index numbers of prices showing changes with respect to time, although methods described can be applied to other cases. In general, the present level of prices is compared with the level of prices in the past. The present period is called the current period and some period in the past is called the base period.

Index Numbers:

Index numbers are statistical measures designed to show changes in a variable or group of related variables with respect to time, geographic location or other characteristics such as income, profession, etc. A collection of index numbers for different years, locations, etc., is sometimes called an index series.

Simple Index Number- A simple index number is a number that measures a relative change in a single variable with respect to a base.

Composite Index Number- A composite index number is a number that measures an average relative changes in a group of relative variables with respect to a base.

Uses of Index Numbers

The main uses of index numbers are given below:

· Index numbers are used in the fields of commerce, meteorology, labour, industrial, etc.

· The index numbers measure fluctuations during intervals of time, group differences of geographical position of degree etc.

· They are used to compare the total variations in the prices of different commodities in which the unit of measurements differs with time and price etc.

· They measure the purchasing power of money.

· They are helpful in forecasting the future economic trends.

· They are used in studying difference between the comparable categories of animals, persons or items.

· Index numbers of industrial production are used to measure the changes in the level of industrial production in the country.

· Index numbers of import prices and export prices are used to measure the changes in the trade of a country.

· The index numbers are used to measure seasonal variations and cyclical variations in a time series.

Nature of Index Number

· Index numbers are specified average used for comparison in situation where two or more series are expressed in different units or represent different items. E. g. Consumer price index representing prices of various items or the index of industrial production representing various commodities produced.

· Index number measure the net change in a group of related variable over a period of time.

· Index number measure the effect of change over a period of time, across the range of industries, geographical regions or countries.

· The consumption of the index number is carefully planned according to the purpose of their computation, collection of data and application of appropriate method, assigning of correct weightages and formula.

Types of Index numbers:

There are various types of index numbers, but in brief, we shall take three kinds and they are

(a) Price Index, (b) Quantity Index and (c) Value Index

a) Price Index: For measuring the value of money, in general, price index is used. It is an index number which compares the prices for a group of commodities at a certain time as at a place with prices of a base period. There are two price index numbers such as whole sale price index numbers and retail price index numbers. The wholesale price index reveals the changes into general price level of a country, but the retail price index reveals the changes in the retail price of commodities such as consumption of goods, bank deposits, etc.

b) Quantity Index: Quantity index number is the changes in the volume of goods produced or consumed. They are useful and helpful to study the output in an economy.

c) Value Index: Value index numbers compare the total value of a certain period with total value in the base period. Here total value is equal to the price of commodity multiplied by the quantity consumed.

Notation: For any index number, two time periods are needed for comparison. These are called the Base period and the Current period. The period of the year which is used as a basis for comparison is called the base year and the other is the current year. The various notations used are as given below:

P1 = Price of current year P0 = Price of base year

q1 = Quantity of current year q0 = Quantity of base year

Problems in the construction of index numbers

No index number is an all-purpose index number. Hence, there are many problems involved in the construction of index numbers, which are to be tackled by an economist or statistician. They are:

1. Purpose of the index numbers

2. Selection of base period

3. Selection of items

4. Selection of source of data

5. Collection of data

6. Selection of average

7. System of weighting

What to you understand by concept of probability? Explain various theories of probability.

noreply@blogger.com (Manoj Maikhuri) — Sat, 26 Sep 2015 09:28:00 +0000

Introduction to Probability

We live in the world of uncertainties. Man is surrounded by situations which are not fully under his control. The nature commands these situations. A person on a road does not know whether or not he will reach his destination safely. A patient in the hospital is never sure about his survival after a delicate operation. What will be the weather conditions tomorrow, nothing is known with certainty but we always like to have an idea about the weather conditions in future. A flight will be in time, the road will be clear or there will be some traffic jam. We face this of problem in our daily life. Man is always curious to know as to what will happen in future. The things which happen are important for the man today. These things are based on what is called chance or probability. If we have some numerical measure of uncertainty, this measure is called probability. We may find a numerical measure for a bulb to be defective, some numerical measure for the rain to fall. The belief or confidence associated with a certain situation can also be measured. It is also called probability. In statistics there are various situations where uncertainty in involved.

Such situations need the application of probability. Probability is widely and rightly used in statistical decisions. The areas of statistics where probability is used are called the areas of statistical inference. Statistical inference is not possible without the use of probability. Probability is also used in different fields of life where uncertainty is involved. Knowledge of probability is used in space research, astronomy, business, weather studies, economics, genetics and various other fields of life. It is simple to explain various concepts of probability with the help of set theory. Thus we shall use here the set theory notation.

Definition of Probability

Probability is something strange and it has been defined in different manners. We can define probability in objective or subjective manner. Let us first use objective approach to define probability.

The Classical Definition of Probability:

This definition is for equally likely outcomes. If an experiment can produced N mutually exclusive and equally likely outcomes out of which n outcomes are favourable to the occurrence of event A, then the probability of A is denoted by P(A) and is defined as the ratio n/N. Thus the probability of A is given by

This definition can be applied in a situation in which all possible outcomes and the outcomes in the events A can be counted. This definition is due to P.S. Laplace (1749 – 1827). The classical definition is also called the priori definition of probability. The word priori is from prior and is used because the definition of base on the previous knowledge that the outcomes are equally likely. When a coin is tossed, the probability of head is assumed to be ½. This probability of ½ is based on this classical definition of probability. It is assumed that the two faces of the coin are equally likely. In practical life the people do believe that a coin will do justice when it is tossed. In the playgrounds, the participating teams toss the coin to start the match. A coin in which probability of head is assumed to be equal to the probability of tail is called a true or uniform or an unbiased coin. But it is an all assumption. The probability of a certain event is a number which lies between 0 and 1. If the event does not contain any outcome, it is called impossible event and its probability is zero. If the event is as big as the sample space, the probability of the event is one because

When probability of an event is one, it is called a “Sure” or “Certain”, event.

Criticism:
The classical definition of probability has always been criticized for the following reasons:

1. This definition assumes that the outcomes are equally likely. The term equally likely is almost as difficult as the word probability itself. Thus the definition uses the circular reasoning.

2. The definition is not applicable when the numbers of outcomes are not equally likely.

3. The definition is also not applicable when the total number of outcomes is infinite or it is difficult to count the total outcomes or the outcomes favourable to the event. It is difficult to count the fish in the ocean. Thus it is difficult to find the probability of catching a fish of some weight say more than one kilogram.

Example:
One day 20 files were presented to an income tax officer for disposal. Five files contained bogus entries. All the files were thoroughly mixed and there was no indication about bogus files. What is the probability that one file with bogus entries is selected.

Solution:

Here all possible outcomes = 20

Let A be the event that the file has bogus entries.

Thus, number of favourable outcomes = 5

Here we shall apply the classical definition of probability. All the 20 files are assumed to be equally likely for the purpose of selecting a file.

Probability of selecting a file with bogus entries is written as P(A)

Four Perspectives on Probability

Four perspectives on probability are commonly used: Classical, Empirical, Subjective, and Axiomatic.

1) Classical (sometimes called "A priori" or "Theoretical")

This is the perspective on probability that most people first encounter in formal education (although they may encounter the subjective perspective in informal education).

For example, suppose we consider tossing a fair die. There are six possible numbers that could come up ("outcomes"), and, since the die is fair, each one is equally likely to occur. So we say each of these outcomes has probability 1/6. Since the event "an odd number comes up" consists of exactly three of these basic outcomes, we say the probability of "odd" is 3/6, i.e. 1/2.

More generally, if we have a situation (a "random process") in which there are n equally likely outcomes, and the event A consists of exactly m of these outcomes, we say that the probability of A is m/n. We may write this as "P(A) = m/n" for short.

This perspective has the advantage that it is conceptually simple for many situations. However, it is limited, since many situations do not have finitely many equally likely outcomes. Tossing a weighted die is an example where we have finitely many outcomes, but they are not equally likely. Studying people's incomes over time would be a situation where we need to consider infinitely many possible outcomes, since there is no way to say what a maximum possible income would be, especially if we are interested in the future.

2) Empirical (sometimes called "A posteriori" or "Frequentist")

This perspective defines probability via a thought experiment.

To get the idea, suppose that we have a die which we are told is weighted, but we don't know how it is weighted. We could get a rough idea of the probability of each outcome by tossing the die a large number of times and using the proportion of times that the die gives that outcome to estimate the probability of that outcome.

This idea is formalized to define the probability of the event A as

P (A) = the limit as n approaches infinity of m/n,

Where n is the number of times the process (e.g., tossing the die) is performed, and m is the number of times the outcome A happens.

(Notice that m and n stand for different things in this definition from what they meant in Perspective 1.)

In other words, imagine tossing the die 100 times, 1000 times, 10,000 times ... Each time we expect to get a better and better approximation to the true probability of the event A. The mathematical way of describing this is that the true probability is the limit of the approximations, as the number of tosses "approaches infinity" (that just means that the number of tosses gets bigger and bigger indefinitely).

This view of probability generalizes the first view: If we indeed have a fair die, we expect that the number we will get from this definition is the same as we will get from the first definition (e.g., P(getting 1) = 1/6; P(getting an odd number) = 1/2). In addition, this second definition also works for cases when outcomes are not equally likely, such as the weighted die. It also works in cases where it doesn't make sense to talk about the probability of an individual outcome. For example, we may consider randomly picking a positive integer (1, 2, 3 ...) and ask, "What is the probability that the number we pick is odd?" Intuitively, the answer should be 1/2, since every other integer (when counted in order) is odd. To apply this definition, we consider randomly picking 100 integers, then 1000 integers, then 10,000 integers, ... . Each time we calculate what fraction of these chosen integers are odd. The resulting sequence of fractions should give better and better approximations to 1/2.

However, the empirical perspective does have some disadvantages. First, it involves a thought experiment. In some cases, the experiment could never in practice be carried out more than once. Consider, for example the probability that the Dow Jones average will go up tomorrow. There is only one today and one tomorrow. Going from today to tomorrow is not at all like rolling a die. We can only imagine all possibilities of going from today to a tomorrow (whatever that means). We can't actually get an approximation.

A second disadvantage of the empirical perspective is that it leaves open the question of how large n has to be before we get a good approximation. The example linked above shows that, as n increases, we may have some wobbling away from the true value, followed by some wobbling back toward it, so it's not even a steady process.

The empirical view of probability is the one that is used in most statistical inference procedures. These are called frequentist statistics.

3) Subjective

Subjective probability is an individual person's measure of belief that an event will occur. With this view of probability, it makes perfectly good sense intuitively to talk about the probability that the Dow Jones average will go up tomorrow. You can quite rationally take your subjective view to agree with the classical or empirical views when they apply, so the subjective perspective can be taken as an expansion of these other views.

However, subjective probability also has its downsides. First, since it is subjective, one person's probability (e.g., that the Dow Jones will go up tomorrow) may differ from another's. This is disturbing to many people. Still, it models the reality that often people do differ in their judgments of probability.

The second downside is that subjective probabilities must obey certain "coherence" (consistency) conditions in order to be workable. For example, if you believe that the probability that the Dow Jones will go up tomorrow is 60%, then to be consistent you cannot believe that the probability that the Dow Jones will do down tomorrow is also 60%. It is easy to fall into subjective probabilities that are not coherent.

The subjective perspective of probability fits well with Bayesian statistics, which are an alternative to the more common frequentist statistical methods. (This website will mainly focus on frequentist statistics.)

4) Axiomatic

This is a unifying perspective. The coherence conditions needed for subjective probability can be proved to hold for the classical and empirical definitions. The axiomatic perspective codifies these coherence conditions, so can be used with any of the above three perspectives.

The axiomatic perspective says that probability is any function (we'll call it P) from events to numbers satisfying the three conditions (axioms) below. (Just what constitutes events will depend on the situation where probability is being used.)

The three axioms of probability:

i) 0 ≤ P(E) ≤ 1 for every allowable event E. (In other words, 0 is the smallest allowable probability and 1 is the largest allowable probability).

ii) The certain event has probability 1. (The certain event is the event "some outcome occurs." For example, in rolling a die, the certain event is "One of 1, 2, 3, 4, 5, 6 comes up." In considering the stock market, the certain event is "The Dow Jones either goes up or goes down or stays the same.")

iii) The probability of the union of mutually exclusive events is the sum of the probabilities of the individual events. (Two events are called mutually exclusive if they cannot both occur simultaneously. For example, the events "the die comes up 1" and "the die comes up 4" are mutually exclusive, assuming we are talking about the same toss of the same die. The union of events is the event that at least one of the events occurs. For example, if E is the event "a 1 comes up on the die" and F is the event "an even number comes up on the die," then the union of E and F is the event "the number that comes up on the die is either 1 or even."

If we have a fair die, the axioms of probability require that each number comes up with probability 1/6: Since the die is fair, each number comes up with the same probability. Since the outcomes "1 comes up," "2 comes up," ..."6 come up" are mutually exclusive and their union is the certain event, Axiom III says that

P (1 comes up) + P (2 comes up) + ... + P (6 comes up) = P (the certain event),

Which is 1 (by Axiom 2), since all six probabilities on the left are equal, that common probability must be 1/6.

Explain the meaning and type of ‘Data’ as applicable in any business. How would you classify and tabulate the data, support your answer with example

noreply@blogger.com (Manoj Maikhuri) — Wed, 23 Sep 2015 09:47:00 +0000

Everybody collects, interprets and uses information, much of it in numerical or statistical forms in day-to-day life. It is a common practice that people receive large quantities of information everyday through conversations, televisions, computers, the radios, newspapers, posters, notices and instructions. It is just because there is so much information available that people need to be able to absorb, select and reject it. In everyday life, in business and industry, certain statistical information is necessary and it is independent to know where to find it how to collect it. As consequences, everybody has to compare prices and quality before making any decision about what goods to buy. As employees of any firm, people want to compare their salaries and working conditions, promotion opportunities and so on. In time the firms on their part want to control costs and expand their profits.

One of the main functions of statistics is to provide information which will help on making decisions. Statistics provides the type of information by providing a description of the present, a profile of the past and an estimate of the future. The following are some of the objectives of collecting statistical information.

1. To describe the methods of collecting primary statistical information.

2. To consider the status involved in carrying out a survey.

3. To analyse the process involved in observation and interpreting.

4. To define and describe sampling.

5. To analyse the basis of sampling.

6. To describe a variety of sampling methods.

Statistical investigation is a comprehensive and requires systematic collection of data about some group of people or objects, describing and organizing the data, analyzing the data with the help of different statistical method, summarizing the analysis and using these results for making judgements, decisions and predictions. The validity and accuracy of final judgement is most crucial and depends heavily on how well the data was collected in the first place. The quality of data will greatly affect the conditions and hence at most importance must be given to this process and every possible precaution should be taken to ensure accuracy while collecting the data.

“Data is any group of observation or measurement related to the area of a business interest and to be used for decision making.”

Nature of data:

It may be noted that different types of data can be collected for different purposes. The data can be collected in connection with time or geographical location or in connection with time and location. The following are the three types of data:

1. Time series data.

2. Spatial data

3. Spacio-temporal data.

1. Time series data:

It is a collection of a set of numerical values, collected over a period of time. The data might have been collected either at regular intervals of time or irregular intervals of time.

Example 1:

The following is the data for the three types of expenditures in rupees for a family for the four years 2001,2002,2003,2004.

Year	Food	Education	Others	Total
2001	3000	2000	3000	8000
2002	3500	3000	4000	10500
2003	4000	3500	5000	12500
2004	4500	5000	6000	16000

2. Spatial Data:

If the data collected is connected with that of a place, then it is termed as spatial data. For example, the data may be

i) Number of runs scored by a batsman in different test matches in a test series at different places

ii) District wise rainfall in Tamilnadu

iii) Prices of silver in four metropolitan cities

Example 2:

The population of the southern states of India in 1991.

State	Population
Tamilnadu	5,56,38,318
Andhra Pradesh	6,63,04,854
Karnataka	4,48,17,398
Kerala	2,90,11,237
Pondicherry	7,89,416

If the data collected is connected to the time as well as place then it is known as spacio temporal data.

3. Spacio Temporal Data:

Example 3:

State	Population
State	1981	1991
Tamilnadu	4,82,97,456	5,56,38,318
Andhra Pradesh	5,34,03,619	6,63,04,854
Karnataka	3,70,43,451	4,48,17,398
Kerala	2,54,03,217	2,90,11,237
Pondicherry	6,04,136	7,89,416

Any statistical data can be classified under two categories depending upon the sources utilized. These categories are,

Categories of data:

1. Primary data 2. Secondary data

1. Primary data:

Primary data is the one, which is collected by the investigator himself for the purpose of a specific inquiry or study. Such data is original in character and is generated by survey conducted by individuals or research institution or any organisation.

Example 4:

If a researcher is interested to know the impact of noon-meal scheme for the school children, he has to undertake a survey and collect data on the opinion of parents and children by asking relevant questions. Such a data collected for the purpose is called primary data.

The primary data can be collected by the following five methods.

i) Direct personal interviews.

ii) Indirect Oral interviews.

iii) Information from correspondents.

iv) Mailed questionnaire method.

v) Schedules sent through enumerators.

2. Secondary Data:

Secondary data are those data which have been already collected and analysed by some earlier agency for its own use; and later the same data are used by a different agency. According to W. A. Neiswanger, ‘ A primary source is a publication in which the data are published by the same authority which gathered and analysed them. A secondary source is a publication, reporting the data which have been gathered by other authorities and for which others are responsible’.

Sources of Secondary data:

In most of the studies the investigator finds it impracticable to collect first-hand information on all related issues and as such he makes use of the data collected by others. There is a vast amount of published information from which statistical studies may be made and fresh statistics are constantly in a state of production.

The sources of secondary data can broadly be classified under two heads:

i) Published sources, and

ii) Unpublished sources.

Classification of Data:

The collected data, also known as raw data or ungrouped data are always in an un organised form and need to be organised and presented in meaningful and readily comprehensible form in order to facilitate further statistical analysis. It is, therefore, essential for an investigator to condense a mass of data into more and more comprehensible and assimilable form. The process of grouping into different classes or sub classes according to some characteristics is known as classification, tabulation is concerned with the systematic arrangement and presentation of classified data. Thus classification is the first step in tabulation.

For Example, letters in the post office are classified according to their destinations viz., Delhi, Madurai, Bangalore, Mumbai etc.,

Objects of Classification:

The following are main objectives of classifying the data:

i) It condenses the mass of data in an easily assimilable form.

ii) It eliminates unnecessary details.

iii) It facilitates comparison and highlights the significant aspect of data.

iv) It enables one to get a mental picture of the information and helps in drawing inferences.

v) It helps in the statistical treatment of the information collected.

Types of Classification:

Statistical data are classified in respect of their characteristics. Broadly there are four basic types of classification namely

a) Chronological classification

b) Geographical classification

c) Qualitative classification

d) Quantitative classification

a) Chronological classification:

In chronological classification the collected data are arranged according to the order of time expressed in years, months, weeks, etc. The data is generally classified in ascending order of time. For example, the data related with population, sales of a firm, imports and exports of a country are always subjected to chronological classification.

Example 5:

The estimates of birth rates in India during 1970 – 76 are

Year	1970	1971	1972	1973	1974	1975	1976
Birth Rate	36.8	36.9	36.6	34.6	34.5	35.2	34.2

In this type of classification the data are classified according to geographical region or place. For instance, the production of paddy in different states in India, production of wheat in different countries etc.

b) Geographical classification:

Example 6:

Country	America	China	Denmark	France	India
Yield of wheat in (kg/acre)	1925	893	225	439	862

In this type of classification data are classified on the basis of same attributes or quality like sex, literacy, religion, employment etc., Such attributes cannot be measured along with a scale.

c) Qualitative classification:

For example, if the population to be classified in respect to one attribute say sex, then we can classify them into two namely that of males and females. Similarly, they can also be classified into ‘employed’ or ‘unemployed’ on the basis of another attribute ‘employment’.

Thus when the classification is done with respect to one attribute, which is dichotomous in nature, two classes are formed, one possessing the attribute and the other not possessing the attribute. This type of classification is called simple or dichotomous classification.

A simple classification may be shown as:

The classification, where two or more attributes are considered and several classes are formed, is called a manifold classification. For example, if we classify population simultaneously with respect to two attributes, e.g sex and employment, then population are first classified with respect to ‘sex’ into ‘ males’ and ‘ females’ . Each of these classes may then be further classified into ‘employment’ and ‘ unemployment’ on the basis of attribute ‘ employment’ and as such Population are classified into four classes namely.

i) Male employed

ii) Male unemployed

iii) Female employed

iv) Female unemployed

Still the classification may be further extended by considering other attributes like marital status etc. This can be explained by the following chart:

d) Quantitative classification:

Quantitative classification refers to the classification of data according to some characteristics that can be measured such as height, weight, etc., For example the students of a college may be classified according to weight as given below.

Weight (in lbs)	No of Students
90-100	50
100-110	200
110-120	260
120-130	360
130-140	90
140-150	40
Total	1000

In this type of classification there are two elements, namely (i) the variable (i.e) the weight in the above example, and (ii) the frequency in the number of students in each class. There are 50 students having weights ranging from 90 to 100 lb, 200 students having weight ranging between 100 to 110 lb and so on.

Tabulation:

Tabulation is the process of summarising classified or grouped data in the form of a table so that it is easily understood and an investigator is quickly able to locate the desired information. A table is a systematic arrangement of classified data in columns and rows. Thus, a statistical table makes it possible for the investigator to present a huge mass of data in a detailed and orderly form. It facilitates comparison and often reveals certain patterns in data which are otherwise not obvious. Classification and ‘Tabulation’, as a matter of fact, are not two distinct processes. Actually they go together. Before tabulation data are classified and then displayed under different columns and rows of a table.

Advantages of Tabulation:

Statistical data arranged in a tabular form serve following objectives:

i) It simplifies complex data and the data presented are easily understood.

ii) It facilitates comparison of related facts.

iii) It facilitates computation of various statistical measures like averages, dispersion, correlation etc.

iv) It presents facts in minimum possible space and unnecessary repetitions and explanations are avoided. Moreover, the needed information can be easily located.

v) Tabulated data are good for references and they make it easier to present the information in the form of graphs and diagrams.

Preparing a Table:

The making of a compact table itself an art. This should contain all the information needed within the smallest possible space. What the purpose of tabulation is and how the tabulated information is to be used are the main points to be kept in mind while preparing for a statistical table. An ideal table should consist of the following main parts:

1. Table number 2. Title of the table

3. Captions or column headings 4. Stubs or row designation

5. Body of the table 6. Footnotes

7. Sources of data

Table Number: A table should be numbered for easy reference and identification. This number, if possible, should be written in the centre at the top of the table. Sometimes it is also written just before the title of the table.

Title: A good table should have a clearly worded, brief but unambiguous title explaining the nature of data contained in the table. It should also state arrangement of data and the period covered. The title should be placed centrally on the top of a table just below the table number (or just after table number in the same line).

Captions or column Headings: Captions in a table stand for brief and self explanatory headings of vertical columns. Captions may involve headings and sub-headings as well. The unit of data contained should also be given for each column. Usually, a relatively less important and shorter classification should be tabulated in the columns.

Stubs or Row Designations: Stubs stands for brief and self explanatory headings of horizontal rows. Normally, a relatively more important classification is given in rows. Also a variable with a large number of classes is usually represented in rows. For example, rows may stand for score of classes and columns for data related to sex of students. In the process, there will be many rows for scores classes but only two columns for male and female students.

A model structure of a table is given below:

<Table Number> <Title of the Table>

Sub Heading	Caption Headings	Total
Sub Heading	Caption Sub-Headings	Total
Stub Sub-Headings	Body
Total
Foot notes: Sources Note:

Body: The body of the table contains the numerical information of frequency of observations in the different cells. This arrangement of data is according to the description of captions and stubs.

Footnotes: Footnotes are given at the foot of the table for explanation of any fact or information included in the table which needs some explanation. Thus, they are meant for explaining or providing further details about the data, that have not been covered in title, captions and stubs.

Sources of data:

Lastly one should also mention the source of information from which data are taken. This may preferably include the name of the author, volume, page and the year of publication. This should also state whether the data contained in the table is of ‘primary or secondary’ nature.

Requirements of a Good Table:

A good statistical table is not merely a careless grouping of columns and rows but should be such that it summarizes the total information in an easily accessible form in minimum possible space. Thus while preparing a table, one must have a clear idea of the information to be presented, the facts to be compared and he points to be stressed.

Though, there is no hard and fast rule for forming a table yet a few general point should be kept in mind:

1. A table should be formed in keeping with the objects of statistical enquiry.

2. A table should be carefully prepared so that it is easily understandable.

3. A table should be formed so as to suit the size of the paper. But such an adjustment should not be at the cost of legibility.

4. If the figures in the table are large, they should be suitably rounded or approximated. The method of approximation and units of measurements too should be specified.

5. Rows and columns in a table should be numbered and certain figures to be stressed may be put in ‘box’ or ‘circle’ or in bold letters.

6. The arrangements of rows and columns should be in a logical and systematic order. This arrangement may be alphabetical, chronological or according to size.

7. The rows and columns are separated by single, double or thick lines to represent various classes and sub-classes used. The corresponding proportions or percentages should be given in adjoining rows and columns to enable comparison. A vertical expansion of the table is generally more convenient than the horizontal one.

8. The averages or totals of different rows should be given at the right of the table and that of columns at the bottom of the table. Totals for every sub-class too should be mentioned.

9. In case it is not possible to accommodate all the information in a single table, it is better to have two or more related tables.

Type of Tables:

Tables can be classified according to their purpose, stage of enquiry, nature of data or number of characteristics used. On the basis of the number of characteristics, tables may be classified as follows:

1. Simple or one-way table 2. Two way table

3. Manifold table

1. Simple or one-way Table:

A simple or one-way table is the simplest table which contains data of one characteristic only. A simple table is easy to construct and simple to follow. For example, the blank table given below may be used to show the number of adults in different occupations in a locality.

The number of adults in different occupations in a locality

Occupations	No. Of Adults

Total

A table, which contains data on two characteristics, is called a two-way table. In such case, therefore, either stub or caption is divided into two co-ordinate parts. In the given table, as an example the caption may be further divided in respect of ‘sex’. This subdivision is shown in two-way table, which now contains two characteristics namely, occupation and sex.

2. Two-way Table:

The umber of adults in a locality in respect of occupation and sex

Occupation	No. of Adults		Total
Occupation	Male	Female	Total

Total

Thus, more and more complex tables can be formed by including other characteristics. For example, we may further classify the caption sub-headings in the above table in respect of “marital status”, “religion” and “socio-economic status” etc. A table, which has more than two characteristics of data is considered as a manifold table. For instance, table shown below shows three characteristics namely occupation, sex and marital status.

3. Manifold Table:

Occupation	No. of Adults						Total
	Male			Female
	M	U	Total	M	U	Total

Total
Foot note: M Stands for Married and U stands for unmarried.

Manifold tables, though complex are good in practice as these enable full information to be incorporated and facilitate analysis of all related facts. Still, as a normal practice, not more than four characteristics should be represented in one table to avoid confusion. Other related tables may be formed to show the remaining characteristics

Explain Packaging and Sales Promotion, state the purpose & types of Packaging.

noreply@blogger.com (Manoj Maikhuri) — Sat, 10 May 2014 03:48:00 +0000

PACKAGING-

Packaging is the science, art & technology of enclosing or protecting product for distribution, storage, Sale, and use. Packaging also refers to the process of designing, evaluation and production of packages. Packaging is heavily integrated into our daily lives. We see it all around us on everyday items such as chocolate bars and potato chips packets- but packaging also provides us with a recognizable logo or packaging. We instantly know what those goods are inside.

The purpose of packaging:

Packaging has several objectives-

Physical protection: The object enclosed in the package may require protection from, among other things, shock, vibration, compression, temperature etc.

Barrier protection: A barrier from oxygen, water vapor, dust etc., is often required package permeability is a critical factor in design. Some packages contain desiccants or oxygen observers to help extend shelf life. Keeping the contents clean, fresh, and safe for the intended shelf life is a primary function.

Containment or Agglomeration: Small objects are typically grouped together in one package for reasons of efficiency. For example, a single box of 1000 of pencils requires less physical handling than 1000 single pencils.

Information transmission: Package and labels communicates how to use transport, recycle, or dispose of the package of product.

Marketing: The packaging can be used by marketers to encourage potential buyers to purchases the product. Package design has been an important and a constantly evolving phenomenon for dozens of years.

Conveniences: Package can have features which add convenience in distribution, handling, display, sale, opening re-closing, use and reuse.

Portion control: Single serving or single dosage packaging has a precise amount of contents to control uses. Bulk commodities (such as salt) can be divided into packages that are a more suitable size for individual households.

Types of Packaging

It is sometime convenient to categories size packages by type of function-

Primary packaging: is the material that first envelops the product and holds it. This usually is the smallest unit of distribution or use and is the package which is in directed with the contents.

Secondary packaging: is outside the primary packaging perhaps used to group primary package together.

Tertiary packaging: is used for bulk handling and transport shipping.

SALES PROMOTION-

Sales promotion is the best tool for short-term results. Company can use sales promotion as a tool to sale the old stock. Sales promotion can bring quick response from customers. Sales promotion is the offering of short-term incentives to customers. Different types of consumer sales promotion tools can be used by marketers to increase sales. They includes-

o Price promotion

o Coupons

o Gifts

o Money refunds and rebates

o Contests

Price Promotion: price promotion is also known as ‘price discounting’; offer a discount on the normal selling price or more of the product at the same price. These promotions can attract customers to make more purchase. Too much price reduction may affect brand image.

Coupons: are also price reductions in the form of a certificate given to the customer, distributed through magazines, newspapers and sometimes along with products.

Gifts: are also called premiums. They are the products given free of cost. When the product is purchased, there will be a gift inside the pack. Sometime customers have to approach retailers to receive gifts by showing proof of purchase.

Money refund and rebates: a certain amount of money is received by customer as a refund on the price of the product, when purchase proof is shown.

Contests: Contests are small competitions conducted by marketers with the intention of increasing sales. Generally, the contests are in the form of quizzes or games. Prices are offered to those who win contests.

What are Consumer products or goods?

noreply@blogger.com (Manoj Maikhuri) — Thu, 20 Mar 2014 03:31:00 +0000

Product planner needs to think about products and service on three levels. The most basic level is the core products, which addresses the questions: what is the buyer really buying? The core product stands at the centre of the total product. It consist of the core, problems-solving benefits that consumers seek when they by a product or service.

The product planner must next build an actual product around the core product. Actual product may have as many as five characteristics: a quality level, features, designs, a brand name and packaging.

Finally the product planners must build an augmented product around the core and actual product by offering additional consumer service and benefits.

Product and service fall into two broad classes based on the type of consumers that use them consumer product and industrial products. Broadly defined, a product also includes other marketable entity such as organisations, persons, places and ideas.

Consumer products:

Consumer products are those bought by final consumer for personal consumption. Marketers usually classify these goods further based on how consumers go about buying them. Consumer products include convenience products, shopping products, speciality products, and unsought products. These products differ in the ways consumer buy them, and therefore in how they are marketed.

a) Convenience Product:

Convenience products are consumer products and services that the consumer usually buys frequently, immediately, and with a minimum of comparison and buying efforts. Example includes soap, candy, newspapers and fast-foods. Such types of products can be divided further into staples products, impulse products and emergency products. Staples products are the products that consumers by on a regular basis, such as ketchup, tooth-past etc. Impulse products are purchased with little planning or search effort, such as candy bars, biscuit, and magazines. Emergency products, are purchased when their need is urgent such as umbrellas during a rains, boots and shovels during the year’s first snowstorm.

b) Shopping Products:

Shopping Products are less frequently purchased consumer products and service that customer compare carefully on suitability, quality, price and style. When buying shopping products and service, consumers spend much time and effort in gathering information and marketing comparisons. Examples, includes furniture, clothing and hotel services. Shopping products marketers usually distribute their products through fewer outlets but provide deeper sales support to help customers in their comparison effort.

c) Speciality Products:

Speciality Products are consumer products and service with unique characteristics or brand identification for which a significant group of buyers is willing to make a special purchase effort. Example, include specific brands and types of cars, high-priced photographic equipments, designer cloths, and the service of medical or legal specialists.

d) Unsought products:

Unsought products are consumer products that the consumer either does not know about or knows about but does not normally think of buying. Most major new innovations are unsought until the consumer becomes aware of them through advertising. Classic example known but unsought product and service are life insurance and blood donation to the Red-Cross. By their vary nature, unsought products require a lot of advertising, personal selling and marketing effort.

Which all technique are adopted while doing financial forecasting, also explain problems in financial forecasting.

noreply@blogger.com (Manoj Maikhuri) — Wed, 24 Jul 2013 04:43:00 +0000

Technique adopted while doing financial forecasting:

Financial forecasting aims ate predetermining the demand for funds and the avenues where the funds are to be utilized. Thus a systematic projection of the financial data is made in the form of financial statement (fund flow analysis, financial ratio ext). Financial forecasting generates information which is utilized by the management of an enterprise for taking decision s particularly for judging the financial efficiency of the funds and projecting a scale of standards to be followed in the future course. Another important basic objective of financial forecasting is its use as control device. Optimum utilization of funds, by a company can be planned through financial forecasting. Financial forecasting is done by using the following technique

a) Fund flow analysis

b) Pro forma financial statements

c) Cash budget

Fund Flow Analysis: Fund flow analysis is accomplished by preparing a fund flow statement for evaluating the uses of funds and determining the sources of funds to finance those users. Fund flow analysis is done by studying past fund flows and projecting future fund flows. Fund flow statement provides the management of a corporate enterprise complete firsthand knowledge of the financial growth of the enterprise and its resulting financial needs. As a matter of fact funds flow statement is known as the best way to determine as to how to finance those needs. It is useful foot in planning needs.

Pro Forma Financial Statement: preparation of pro forma financial statement is another technique for financial forecasting. In pro forma financial statement, the pro forma balance sheet and profit and loss account are prepared to enable the management to evaluate the performance of the enterprise future financial conditions.

Cash Budget: cash budget is another technique of financial forecasting. It is used to determine short-term cash needs. The liquidity position of an enterprise and degree of business risk involved for planning a realistic margin of safety. It gives clues of the enterprise for adjusting the liquidity cushion, rearranging maturity structure of the debt and making arrangement for availing cash credit facilities from the banks.

Problems in Financial Forecasting:

1) Business environment is frequently changing. Every change reflects upon uncertainty of future and enhances the degree of incompatibility of present decision in future. Therefore the likely margin of error inherent in forecasting the future should be considers in advance avoiding disappointment caused by false results and the loss to be incurred due to inaccuracy attached to the forecast.

2) Pre testing under controlled conditions of forecast should be done by designing alternative forecast for making a better choice and flexibility in decision making by allowing a pick up one out of several forecast.

3) Continuous modification should be made in forecasting to adjust the same against changes in business environment. Many experts hold that forecasting to adjust ed to changes in sales volumes inventory levels, balance of debtors affected by seasonal parameters.

4) to make the forecast reliable and also as a precautionary measure to unpredictability of forecasting results, the corporate enterprise is advised by experts to maintain sufficient cash balance to minimize the risk involved in higher degree of unpredictability associated with forecasting liquidity.

5) Use of mathematical technique can make the forecast more reliable and dependable. These techniques may include a) Simple linear regression method

6) Simple curvilinear regression method

7) Multiple regression models.

Define with the help of a chart working capital management, explain credit policy

noreply@blogger.com (Manoj Maikhuri) — Wed, 17 Jul 2013 09:30:00 +0000

Working capital management is significant fact of financial management.

Working capital management is concerned with making sure we have exactly the right amount of money and lines of credit available to the business at all times. Working capital management involves the relationship between a firm's short-term assets and its short-term liabilities. The goal of working capital management is to ensure that a firm is able to continue its operations and that it has sufficient ability to satisfy both maturing short-term debt and upcoming operational expenses.

The management of working capital involves managing inventories, accounts receivable and payable, and cash. Working Capital is the money used to make goods and attract sales. The less Working Capital used to attract sales, the higher is likely to be the return on investment. Working Capital management is about the commercial and financial aspects of Inventory, credit, purchasing, marketing, and royalty and investment policy. The higher the profit margin, the lower is likely to be the level of Working Capital tied up in creating and selling titles the term working capital refers to the amount of capital which is readily available to an organization.

That is, working capital is the difference between resources in cash or readily convertible into cash (Current Assets) and organizational commitments for which cash will soon be required (Current Liabilities). Current Assets are resources which are in cash or will soon be converted into cash in "the ordinary course of business".

Current Liabilities are commitments which will soon require cash settlement in "the ordinary course of business".

Thus:

WORKING CAPITAL = CURRENT ASSETS - CURRENT LIABILITIES

In a department's Statement of Financial Position, these components of working capital are reported under the following headings:


Current Assets

•   Liquid Assets (cash and bank deposits) 

•   Inventory 

•   Debtors and Receivables 

•   Loans & advances

•   Finished goods

•   Raw material and component

Current Liabilities

•   Bank Overdraft 

•   Creditors and Payables 

•   Other Short Term Liabilities 

•   Trade advances

Sundry creditors Working capital constitutes part of the Crown's investment in a department. Associated with this is an opportunity cost to the Crown. (Money invested in one area may "cost" opportunities for investment in other areas.) If a department is operating with more working capital than is necessary, this over-investment represents an unnecessary cost to the Crown. From a department's point of view, excess working capital means operating inefficiencies.

The objective of working capital management is to maintain the optimum balance of each of the working capital components. This includes making sure that funds are held as cash in bank deposits for as long as and in the largest amounts possible, thereby maximizing the interest earned. However, such cash may more appropriately be "invested" in other assets or in reducing other liabilities.

Working capital management takes place on two levels:

§ Ratio analysis can be used to monitor overall trends in working capital and to identify areas requiring closer management (see Chapter Three).

§ The individual components of working capital can be effectively managed by using various techniques and strategies (see Chapter Four). When considering these techniques and strategies, departments need to recognize that each department has a unique mix of working capital components.

The emphasis that needs to be placed on each component varies according to department. For example, some departments have significant inventory levels; others have little if any inventory. Further more, working capital management is not an end in itself. It is an integral part of the department's overall management. The needs of efficient working capital management must be considered in relation to other aspects of the department's financial and non-financial performance.

FACTORES INFLUENCING THE REQUIREMENT OF WORKING CAPITAL

a) Daily requirement of cash

b) Minimum requirement of raw material for smooth production activities

c) The number of day’s requirement to convert raw material into finished product.

d) The number of day’s credit given to customers

CREDIT POLICY

The credit policy of a company is very important for the business prospects and hence a decision on this should be taken after considering various factors like:

a) Government guidelines

b) Nature of product

c) Competition

d) Customer background

e) Financial position of the company

Credit policy is the actions taken by a business to grant, monitor and collect the cash for outstanding accounts receivable. Now I m going to explain the credit policy considering od ‘X’ ltd. FOLLOWING three credit policies were under consideration of Xltd. We have to find out which of the policies would be beneficial to the company considering the net profit for the year concerned

From the above working, we can come to conclusion that credit policy ‘c’ would give more profit hence to be ranked as I, credit policy as ‘B’ AS II and credit policy ‘A’ AS III

Indian Financial Market

noreply@blogger.com (Manoj Maikhuri) — Wed, 10 Jul 2013 03:58:00 +0000

Capital market is the market brings together all the providers and users of capital, all the financial products, like stocks and bonds which make the transfer of capital possible, and all the people and organizations which support the process. The structures of Indian capital market

From the above chart we can observe that Indian capital market is mainly divided into two -Organized and Unorganized. Under organized we have money lenders and indigenous bankers. In the organized sector we have a host of agencies and systems, security market under which there are new issues and further government bonds and corporate securities. Then there is stock market under which again government bonds and corporate securities and under corporate securities there are again bonds and shares. In addition to these under organized sector there are banks and non-banking financial institution exclusively engaged in capital financing. The organized sector is under the direct control of reserve bank of India. The role of major financial institution in India

Industrial Finance Corporation of India (IFCI)

Industrial Finance Corporation of India Ltd. (IFCI), the first development finance institution was set up in 1948 under the IFCI Act to pioneer long-term institutional credit to medium and large industries. With a view to achieving greater operational flexibility and access to capital market, the undertaking of IFCI was transferred to and vested in a new company called Industrial Finance Corporation of India Ltd. (since renamed as IFCI Ltd.) with effect from July 1, 1993. IFCI provides financial assistance to industry by way of rupee and foreign currency loans, underwriting/direct subscription to shares and debentures and guarantees, equipment finance, buyers’ and suppliers’ credit, equipment leasing and finance to leasing and hire-purchase companies. Over the years, it has diversified its activities by setting up several subsidiaries. In tandem with changing business conditions and environment, the wholly-owned subsidiaries viz. IFCI Custodial Services and IFCI Investors Services Ltd.

Classification of Costs

noreply@blogger.com (Manoj Maikhuri) — Wed, 05 Jun 2013 03:41:00 +0000

Classification of costs is the process of grouping costs according to their common characteristics. In order to identify costs with cost centre or cost units a suitable classification of costs is of much significance, cost may be classified from different viewpoints which are follows according to nature of items cost may be of 2 types

1) Direct cost

2) Indirect costs

Direct cost: A cost that can be directly traced to producing specific goods or services. Direct cost refers to those costs which can be easily identified with product, process or department, material used and labour employed in manufacturing in article or in a particular process of production are common example of direct cost. Costs which are traceable to the specific activity. Labour and material inputs are the most common direct costs but there can also be other traceable costs such as marketing, selling or administrative type costs. Direct costs together with indirect costs determine the total cost of providing a good or services.

Direct cost is a cost that can easily and economically be identified either with an individual unit of production or with a responsibility centre. Direct cost is a cost which can be allocated in full to a product, service, customer, and cost centre or business activity. A group of staff dedicated to developing a customer application is an example.

Indirect cost: costs which are not traceable to specific activities. Examples of indirect costs may be: salaries of chairs and administrative staff, regular telephone services, supplies not specifically charged out. Indirect costs together with direct costs, determine the total cost of providing a particular good or service. Indirect cost is also known as facilities and administrative costs. The costs of operations which cannot be assigned to specific projects, such as electricity and central administrative services, sometimes referred to as "overhead”. Indirect cost is a cost that cannot be easily and conveniently traced to the particular cost object under consideration.

Cost from the view point of their variability classified into 3 types

1) Fixed Cost: A production cost which does not vary with the volume of output eg. Administrative costs. Fixed cost is a cost that remains constant in the short term when the quantity produced increases or decreases within certain scales of production. Fixed costs are operating expenses that are incurred when providing necessities for doing business and have no relation to the volume of production and sales (as opposed to "variable costs"). Examples are rent, property taxes, and interest expense.

2) Variable costs: Any costs that change significantly with the level of output eg. Cost of materials is variable cost. Variable cost is also known as , operating costs, prime costs, on costs and direct costs, are costs which vary directly with the rate of output, for example, labour, fuel, power and cost of raw material.

3) Semi variable costs: some cots have tendency to vary with changes in the volume of output or sales, but not in direct proportion to the change. These costs are partly fixed and partly variable and as such these cots are known as semi-variable costs. A Cost that is partly fixed and partly variable at the same time. Total costs are an example of a semi variable cost

From the view point of their controllability cost may be classified as:

1) Controllable costs: controllable costs refer to those costs which can be influenced by the action of a specified member of an undertaking.

2) Uncontrollable costs: uncontrollable costs, on the other hand, refer to those costs which cannot be influenced by the action of a specified member of an undertaking.

From the view point of their normality:

1) Normal costs: normal costs refer to those costs which are normally incurred ata given level of output in the conditions in which that level of output is normally attained. Such costs cannot be avoided at all.

2) Abnormal costs: abnormal or avoidable costs refer to those costs which are not normally incurred at a given level of output in the condition in which the level of output is attained. From the point of relevance decision making and control cost may be defined as:

i) Sunk costs: sunk costs are costs which cannot be recovered. Costs incurred in the past which are irretrievable and therefore not relevant to current decisions. For example, a small bakery might buy an over at a fixed cost, but which it could sell at some future date should it want to. It also might pay out a large amount in advertising its services. However, this latter cost could not be recovered later on - once paid for, the advertising has gone, whether or not the promotion is successful. Sunk costs represent a barrier to entry in an industry because they scare potential entrants from entering - should they fail, they would have. In economics and in business decision-making, sunk costs are costs that have already been incurred and which cannot be recovered to any significant degree. Sunk costs are sometimes contrasted with incremental costs, which are the costs that will change due to the proposed course of action. In microeconomic theory, only incremental costs are relevant to a decision. If we let sunk costs influence our decisions, we will not be assessing a proposal exclusively on its own merits.

ii) Out of pocket costs: out of pocket cost refers to those costs which signify the present of future cash expenditure regarding a certain decision that will vary depending upon the nature of decision made.

iii) Opportunity costs: opportunity cost is a term used in economics, to mean the cost of something in terms of an opportunity foregone (and the benefits that could be received from that opportunity), or the most valuable foregone alternative. For example, if a city decides to build a hospital on vacant land that it owns, the opportunity cost is some other thing that might have been done with the land and construction funds instead. In building the hospital, the city has forgone the opportunity to build a sporting cent.

iv) Imputed costs: imputed costs refer to those costs which are not included in costs but are considered for making management decision.

v) Differential costs: these refer to difference in total costs between two alternatives according to point of view of function cost may be defined as:

· Production costs: production costs refer to those costs which arise in the course of production from the acquisition of raw material till the finished products have been turned out.

· Administration costs: administration costs refer to those costs which are required for formulating the policy, directing the organization, and controlling the operation of an undertaking.

· Selling costs: selling costs refer to those costs which are incurred to create and stimulate demand and to secure orders.

· Distribution costs: distribution costs refer to the costs of the sequence of operation which starts with making the packed product available for dispatch and ends with making the reconditioned returned empty package available for re-use.

Scope of Managerial Research

noreply@blogger.com (Manoj Maikhuri) — Fri, 17 May 2013 06:08:00 +0000

· Management Research is an international journal dedicated to advancing the understanding of management in private and public sector organizations through empirical investigation and theoretical analysis.

· Management Research attempts to provide an international dialogue between researchers and thereby improve the understanding of the nature of management in different settings and, consequently, achieve a reasonable transfer of research results to management practice in several contexts.

· Management Research is especially dedicated to foster the general advancement of management scholarship among iberoamerican scholars and/or those academics interested in iberoamerican issues. Iberoamerica is defined broadly to include all of Latin America, Latino populations in North America, and Spain/Portugal. However, submissions are encouraged from all management scholars regardless of ethnicity or national origin and manuscripts should not be limited to themes dealing with iberoamerican populations.

· Management Research is a multidisciplinary outlet open to contributions of high quality, from any perspective relevant to the field and from any country.

· Management Research intends to become a supranational journal which gives special attention to national and cultural similarities and differences world-wide. This is reflected by its international editorial board and publisher and its sponsorship by the Iberoamerican Academy of Management.

· Management Research is open to a variety of perspectives, including those that seek to improve the effectiveness of, as well, as those critical of, management and organizations.

· Management Research is receptive to research across a broad range of management topics such as human resource management, organizational behavior, organization theory, strategic management, corporate governance, and managerial economics. The management and organization contributions present in Management Research articles can also be grounded in the basic social disciplines of economics, psychology, or sociology. Articles can be empirical, theoretical or measurement oriented. Conceptual articles should provide new theoretical insights that can advance our understanding of management and organizations. Empirical articles should have well-articulated and strong theoretical foundations. All types of empirical methods -quantitative, qualitative or combinations- are acceptable.

· Management Research encourages the interplay between theorizing the empirical research in the belief that they should be mutually informative.

· Management Research is especially interested in new data sources. That includes models that test new theory and expand our sample pools by including alternative approaches to sampling and measurement and samples drawn from non-traditional sources (e.g., from iberoamerican firms), and the examination of the validity and reliability of such samples.

· Management Research publishes only original research as articles or research notes. Manuscripts will be considered for publication with the understanding that their contents are not under consideration for publication elsewhere. Prior presentation at conference or concurrent consideration for presentation at a conference does not disqualify a manuscript from consideration by Management Research.

Explain the various measure of central tendency?

noreply@blogger.com (Manoj Maikhuri) — Mon, 06 May 2013 03:19:00 +0000

In statistics, the general level, characteristic, or typical value that is representative of the majority of cases. Among several accepted measures of central tendency employed in data reduction, the most common are

· The arithmetic mean (simple average)

· The median

· The mode.

FOR EXAMPLE, one measure of central tendency of a group of high school students is the average (mean) age of the students.

Central tendency is a term used in some fields of empirical research to refer to what statisticians sometimes call "location". A "measure of central tendency" is either a location parameter or a statistic used to estimate a location parameter.

Examples include:-

· Arithmetic mean, the sum of all data divided by the number of observations in the data set.

· Median, the value that separates the higher half from the lower half of the data set.

· Mode, the most frequent value in the data set.

Measures of central tendency, or "location", attempt to quantify what we mean when we think of as the "typical" or "average" score in a data set.

The concept is extremely important and we encounter it frequently in daily life. For example, we often want to know before purchasing a car its average distance per litre of petrol. Or before accepting a job, you might want to know what a typical salary is for people in that position so you will know whether or not you are going to be paid what you are worth. Or, if you are a smoker, you might often think about how many cigarettes you smoke "on average" per day. Statistics geared toward measuring central tendency all focus on this concept of "typical" or "average."

As we will see, we often ask questions in psychological science revolving around how groups differ from each other "on average". Answers to such a question tell us a lot about the phenomenon or process we are studying

Arithmetic Mean:

the sum of the numbers divided by the number of values, often called the ”Average”

· Add all values together

· Divide by the number of values to obtain the mean.

Example:

The Mean of 07, 12, 24, 20, 19 is:

(07+12+24+20+19)/05 = 16.4

Median:

· The value which divides the values into two equal halves, which half of the values being lower than the median and half higher than the median

· Short the values in to ascending order.

· If you have an odd number of values, the median is the middle value.

· If you have an even number of values, the median is the arithmetic mean of the two middle values.

Example:

The median of the above series is:

In Ordinary form: 07, 12, 24, 20, 19

In Ascending order: 07, 12, 19, 20, 24

Median is 19

Mode:

The most frequently occurring value (or Values)

· Calculate the frequencies for all of the values in the data

· The mode is the value (or values) with the highest frequency.

Example:

For individuals having the following ages:

18, 18, 19, 20, 20, 20, 21 and 23

The mode is 20 (most frequently occurring value)

What is a case study method? Briefly explain assumption and major steps in case study method.

noreply@blogger.com (Manoj Maikhuri) — Wed, 01 May 2013 04:01:00 +0000

Case study research excels at bringing us to an understanding of a complex issue or object and can extend experience or add strength to what is already known through previous research. Case studies emphasize detailed contextual analysis of a limited number of events or conditions and their relationships. Researchers have used the case study research method for many years across a variety of disciplines.

Researcher Robert K. Yin defines the case study research method as an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used.

Many well-known case study researchers such as Robert E. Stake, Helen Simons, and Robert K. Yin have written about case study research and suggested techniques for organizing and conducting the research successfully. This introduction to case study research draws upon their work and proposes six steps that should be used:

• Determine and define the research questions

• Select the cases and determine data gathering and analysis techniques

• Prepare to collect the data

• Collect data in the field

• Evaluate and analyze the data

• Prepare the report

Step 1: Determine and Define the Research Questions

The first step in case study research is to establish a firm research focus to which the researcher can refer over the course of study of a complex phenomenon or object. The researcher establishes the focus of the study by forming questions about the situation or problem to be studied and determining a purpose for the study.

The researcher investigates the object of the case study in depth using a variety of data gathering methods to produce evidence that leads to understanding of the case and answers the research questions are targeted to a limited number of events or conditions and their inter-relationships ("how" or "why").

Careful definition of the questions at the start pinpoints where to look for evidence and helps determine the methods of analysis to be used in the study. The literature review, definition of the purpose of the case study, and early determination of the potential audience for the final report guide how the study will be designed, conducted, and publicly reported.

Step 2: Select the Cases and Determine Data Gathering and Analysis

Techniques during the design phase of case study research, the researcher determines what approaches to use in selecting single or multiple real-life cases to examine in depth and which instruments and data gathering approaches to use. When using multiple cases, each case is treated as a single case. Each case’s conclusions can then be used as information contributing to the whole study, but each case remains a single case.

The researcher determines in advance what evidence to gather and what analysis techniques to use with the data to answer the research questions. Data gathered is normally largely qualitative, but it may also be quantitative. Tools to collect data can include surveys, interviews, documentation review, observation, and even the collection of physical artifacts. The researcher must use the designated data gathering tools systematically and properly in collecting the evidence. Throughout the design phase, researchers must ensure that the study is well constructed to ensure construct validity, internal validity, external validity, and reliability.

Exemplary case study design ensures that the procedures used are well documented and can be repeated with the same results over and over again.

Step 3: Prepare to Collect the Data:

Because case study research generates a large amount of data from multiple sources, systematic organization of the data is important to prevent the researcher from becoming overwhelmed by the amount of data and to prevent the researcher from losing sight of the original research purpose and questions.

The investigator training program covers the basic concepts of the study, terminology, processes, and methods, and teaches investigators how to properly apply the techniques being used in the study.

After investigators are trained, the final advance preparation step is to select a pilot site and conduct a pilot test using each data gathering method so that problematic areas can be uncovered and corrected. Researchers need to anticipate key problems and events, identify key people, prepare letters of introduction, establish rules for confidentiality, and actively seek opportunities to revisit and revise the research design in order to address and add to the original set of research questions.

Step 4: Collect Data in the Field:

The researcher must collect and store multiple sources of evidence comprehensively and systematically, in formats that can be referenced and sorted so that converging lines of inquiry and patterns can be uncovered. Researchers carefully observe the object of the case study and identify causal factors associated with the observed phenomenon.

Case study research is flexible, but when changes are made, they are documented systematically.

The researcher may enter some data into a database and physically store other data, but the researcher documents, classifies, and cross-references all evidence so that it can be efficiently recalled for sorting and examination over the course of the study.

Step 5: Evaluate and Analyze the Data:

The researcher examines raw data using many interpretations in order to find linkages between the research object and the outcomes with reference to the original research questions. Throughout the evaluation and analysis process, the researcher remains open to new opportunities and insights.

In all cases, the researcher treats the evidence fairly to produce analytic conclusions answering the original "how" and "why" research questions.

Step 6: Prepare the report:

Exemplary case studies report the data in a way that transforms a complex issue into one that can be understood, allowing the reader to question and examine the study and reach an understanding independent of the researcher.

During the report preparation process, researchers critically examine the document looking for ways the report is incomplete. The researcher uses representative audience groups to review and comment on the draft document. Based on the comments, the researcher rewrites and makes revisions. Some case study researchers suggest that the document review audience include a journalist and some suggest that the documents should be reviewed by the participants in the study.

Assumption of case study method

The case study method is based on several assumptions. The importance assumptions are explained below

Uniformity of human nature

The assumption of uniformity in the basic human nature in spite of the fact that human behaviour may vary according to situations. This assumption underlines the collection of case data.

Nature history of the unit

The assumption of studying the natural history of the unit concerned. It gives the background for the study.

Comprehensive study

The assumption of comprehensive study of the unit concerned

Applicability

Psychologist has stated that some statement about human broadly apply to each individual or to each member of a large group.

Homogeneity

According to Cora Dubois, an Anthropologist, the case study is possible only because of certain basic homogeneity or similarity in evidenced in the mankind.

Major steps of case study method:

Identify the case topic, setting, primary focus, and perspective.
Obtain relevant public background materials and knowledgeable informant insights.
Obtain access, approval, and clarify anonymity issues with key gatekeeper.
Obtain relevant documents, minutes, reports and other appropriate materials.
Develop preliminary chronology of key events leading to controversy or decision and identify key players and issues.
Consider varied perspective and sources of information and pedagogical purpose of the case.
Develop interview protocol (key questions for various informants) and further information to collect. This will evolve further.
Conduct interviews and collect other documents, information and materials.
Develop case outline and style of presentation.
Draft Case: obtain comment and feedback from key gatekeeper. Revise & finalised the case.

What is questionnaire? mention its characteristics and illustrate a sample questionnaire for any product you can choose

noreply@blogger.com (Manoj Maikhuri) — Wed, 24 Apr 2013 03:48:00 +0000

Questionnaire is a method used for collecting data; a set of written questions, which calls for responses on the part of the client; may be self-administered or group-administered. Questionnaires are an inexpensive way to gather data from a potentially large number of respondents. Often they are the only feasible way to reach a number of reviewers large enough to allow statistically analysis of the results.

A well-designed questionnaire that is used effectively can gather information on both the overall performance of the test system as well as information on specific components of the system. If the questionnaire includes demographic questions on the participants, they can be used to correlate performance and satisfaction with the test system among different groups of users. It is important to remember that a questionnaire should be viewed as a multi-stage process beginning with definition of the aspects to be examined and ending with interpretation of the results.

Every step needs to be designed carefully because the final results are only as good as the weakest link in the questionnaire process. Although questionnaires may be cheap to administer compared to other data collection methods, they are every bit as expensive in terms of design time and interpretation. The steps required to design and administer a questionnaire include:

1.  Defining the Objectives of the survey

2.  Determining the Sampling Group

3.  Writing the Questionnaire

4.  Administering the Questionnaire

5.  Interpretation of the Results

This document will concentrate on how to formulate objectives and write the questionnaire. Before these steps are examined in detail, it is good to consider what questionnaires are good at measuring and when it is appropriate to use questionnaires. What can questionnaires measure? Questionnaires are quite flexible in what they can measure, however they are not equally suited to measuring all types of data.

We can classify data in two ways, Subjective vs. Objective and Quantitative vs. Qualitative.

When a questionnaire is administered, the researchers control over the environment will be somewhat limited. This is why questionnaires are inexpensive to administer. This loss of control means the validity of the results is more reliant on the honesty of the respondent. Consequently, it is more difficult to claim complete objectivity with questionnaire data then with results of a tightly controlled lab test.

For example, if a group of participants are asked on a questionnaire how long it took them to learn a particular function on a piece of software, it is likely that they will be biased towards themselves and answer, on average, with a lower than actual time. A more objective usability test of the same function with a similar group of participants may return a significantly higher learning time.

More elaborate questionnaire design or administration may provide slightly better objective data, but the cost of such a questionnaire can be much higher and offset their economic advantage. In general, questionnaires are better suited to gathering reliable subjective measures, such as user satisfaction, of the system or interface in question. Questions may be designed to gather either qualitative or quantitative data. By their very nature, quantitative questions are more exact then qualitative.

For example, the word "easy" and "difficult" can mean radically different things to different people. Any question must be carefully crafted, but in particular questions that assess a qualitative measure must be phrased to avoid ambiguity.

Qualitative questions may also require more thought on the part of the participant and may cause them to become bored with the questionnaire sooner. In general, we can say that questionnaires can measure both qualitative and quantitative data well, but that qualitative questions require more care in design, administration, and interpretation.

When to use a questionnaire?

There is no all-encompassing rule for when to use a questionnaire. The choice will be made based on a variety of factors including the type of information to be gathered and the available resources for the experiment. A questionnaire should be considered in the following circumstances.

a. When resources and money are limited. A Questionnaire can be quite inexpensive to administer. Although preparation may be costly, any data collection scheme will have similar preparation expenses. The administration cost per person of a questionnaire can be as low as postage and a few photocopies. Time is also an important resource that questionnaires can maximize. If a questionnaire is self-administering, such as a e-mail questionnaire, potentially several thousand people could respond in a few days. It would be impossible to get a similar number of usability tests completed in the same short time.

b. When it is necessary to protect the privacy of the participants. Questionnaires are easy to administer confidentially. Often confidentiality is the necessary to ensure participants will respond honestly if at all. Examples of such cases would include studies that need to ask embarrassing questions about private or personal behaviour.

c. When corroborating other findings. In studies that have resources to pursue other data collection strategies, questionnaires can be a useful confirmation tools. More costly schemes may turn up interesting trends, but occasionally there will not be resources to run these other tests on large enough participant groups to make the results statistically significant. A follow-up large scale questionnaire may be necessary to corroborate these earlier results

Characteristics of a Good Questionnaire

•   Questions worded simply and clearly, not ambiguous or vague, must be objective

•   Attractive in appearance (questions spaced out, and neatly arranged)

•   Write a descriptive title for the questionnaire

•   Write an introduction to the questionnaire

•   Order questions in logical sequence

•   Keep questionnaire uncluttered and easy to complete

•   Delicate questions last (especially demographic questions)

•   Design for easy tabulation

•   Design to achieve objectives

•   Define terms

•   Avoid double negatives (I have not any money)

•   Avoid double-barreled questions (this AND that)

• Avoid loaded questions ("Have you stopped beating your wife?")

Give the significance of social research also mentions the different problems of social research and how they are solved?

noreply@blogger.com (Manoj Maikhuri) — Thu, 18 Apr 2013 10:16:00 +0000

Within the last 20 to 25 years, courses in methods of social research have come to occupy an increasingly important role in sociological curricula. It likely that at present every major university offers such courses. This is because growing significance of social research and also growing job opportunities in this field.

In this present age, social science are accruing a scientific method of study for this method, research is an important factor. In the last two or three decades, a social research has become an important subject of the curriculum of sociology.

Social research has therefore, assumed greater importance. Apart from thus, the social science research is essential for proper understanding the society and proper collection and analysis of social facts.

The social research is an effective method. Research laboratory techniques are helping in finding further knowledge, about the subject. Through research only it has been possible to make progress and reach further.

In fact, research is an organized effort to acquire new knowledge. It is based on the past experience and past knowledge. The richer the past knowledge, greater the surely of the results.

Problems of Social Research and their solutions:

In science sociology is assuming a scientific base research has become a part of study, it is not an easy task to predict social behavior because of human nature is ever changing. Problems of scientific research in fact, social research deal with social phenomena which are quite different than natural phenomena. Hence there are fundamental differences between research in social science and that of physical and natural science.

Let us study main difficulties faced by the researcher in the application so scientific methods in social research. Complexity of social data It is a well known that social science studies the human behavior which depends on several factor such as physical, social, temperamental ,psychological, geographical, biological social cultural etc. because of these factors a researcher is generally confused. It is therefore said that because of this complexity of social fatal human beings cannot be put to scientific test.

Problems of concepts:

In social science research, one has to face number of problems among which of:

a) Abstraction

b) Faculty reasoning Plays major role in formulating and defining the concepts and laws.

Problems in interpreting relationship between cause and effects:

In social science research, we generally find interdependent relationship between cause and effect. The cause and effect are one and the same, for example, in underdevelopment countries, the economics development cannot be accelerated due to lack of technical know how and capital cannot be obtained due to underdevelopment of the country.

Dynamic nature of social phenomena

Man is a social animal and human society undergoes constant change. What is true today may not be useful tomorrow. The techniques used in past may prove useless for present ad future studies. On a account of this dynamic nature of social phenomena our task of analyzing data becomes very much complicated and the interferences drawn may be misleading. Problems of maintaining objectivity The problem of impartiality in part of problem of objectivity. It is generally argued that the social scientific are less objective than natural scientific because their own interest affected by the finding of their studies, hence leading to prejudice and bias.

Unpredictability

Predictability is one of the most important characteristics of science. In case of physical science, high degree of predictability is possible but it is not so in case of social data. But this statement is also partially true the social scientist can roughly estimate the behavior of the group.

Difficulty in the verification of the inferences: In social research, the events of social science are non-repetitive and the social science is ill-equipped with their tools to verify inferences.

Difficulty in the use of experimental method: In case of social science research its product being a human being cannot be put to laboratory test. Even if it is done, their responses wouldn’t be natural but subject to the awareness of the artificial condition. Thus social scientist has to watch them in wide world.

Difficulty in the use of experimental method: In case of social science research, its product being a human being cannot be put to lab test. Even if it is done, their responses wouldn’t natural but subject to the awareness of the artificial condition. Thus the social scientist has to watch them in the wide world.

Incapability of being dealt through empirical method: An empirical method cannot be applied in case of social science research as repeated experiment is not possible ,for example, the problem of unbiased sampling selection of data etc.

Problem of inter-disciplinary research

Social science being, inter-disciplinary one i.e. related with, economics, political science and sociology, we cannot draw water-tight compartments for each other social science.

Paucity of funds:

In case of social science research, we generally observed that small amount if finance is made available to them, it is not sufficient to conduct research effectively.

Less resources:

Prof Mitchell has rightly pointed out that social science researcher require less resources in comparing to physical science.

Illustrate multiple activities chart with the help of a diagram. Also explain techniques of work measurement.

noreply@blogger.com (Manoj Maikhuri) — Tue, 09 Apr 2013 04:58:00 +0000

Multiple Activity Charts

Multiple Activity Charts (or Multi-Activity Charts) are a very useful tool for understanding the flow of work in a cyclical process and as a consequence understanding which resource is controlling the overall progress of the work. The tool can be used to model different scenarios to determine the optimum mix of resources for the work.

The example below is based on using a tower crane to lift concrete from the street to a high floor for placing. Each resource is represented by a column; time is on the vertical axis.

Time	Truck & Crew	Crane & Skip	Placing Crew	Finishing Crew
1 min	Fill skip	Skip filling	Wait	Wait
2 min
3 min	Wait	Hoist Skip
4 min		Skip emptying	Place and compact concrete
5 min				Smooth and finish concrete
6 min		Lower skip	Wait
7 min	Fill skip	Skip filling
8 min
9 min	Wait	Hoist Skip
10 min		Skip emptying	Place and compact concrete	Wait
11 min				Smooth and finish concrete
12 min		Lower skip	Wait
13 min	Fill skip	Skip filling

In this example, the crane (or more accurately the crane skip) is critical. On way to reduce the cycle would be to use two skips. One skip could be being filled while the other is emptied and all that would be needed is a few seconds to change over the crane hook when the newly emptied skip is lowered to the ground.

Time	Truck & Crew	Skip 1	Skip 2	Crane	Placing Crew	Finishing Crew
1 min	Fill skip 1	Skip filling
2 min
3 min	Fill skip 2	Hoist Skip	Skip filling	Hoist Skip 1
4 min		Skip emptying		Skip 1 emptying	Place S1 concrete
5 min						Smooth and finish concrete Smooth and finish concrete Smooth and finish…..
6 min		Lower skip		Lower skip 1
7 min	Fill skip 1	Skip filling		Change Hook
8 min			Hoist Skip	Hoist Skip 2
9 min			Skip emptying	Skip 2 emptying	Place S2 concrete
10 min
11 min			Lower skip	Lower skip 2
12 min	Fill skip 2		Skip filling	Change Hook
13 min		Hoist Skip		Hoist Skip 1
14 min		Skip emptying		Skip 1 emptying	Place S1 concrete
15 min

We have now reduced the cycle from 6 minutes to 5 minutes per load at the cost of hiring an additional concrete skip. The cycle could be further reduced if the hook change time is minimised. This may not sound much but if there were 30 skip loads of concrete in the pour and the cycle time could be reduced from 6 minutes to 4, the hour saved would reduce the cost of the pour by around $2,000 to $3,000. The next question though is how would the finishing crew manage? They are now working 100% of the time and arrangements may be needed for the placing crew to help them keep up with the increased pour rate.

Multiple Activity Charts a very simple technique that can provide valuable insights to help optimise any cyclical process that involves several different resources. They can also provide a valuable tool for monitoring progress in critical situations where a detailed understanding of the workflow is needed.

The difference between these types of analysis and bar-charts or CPM networks is the focus is on optimizing resource usage in a repetitive / cyclical process. Once the resource usage and cycle time are optimised, the resulting overall duration can be incorporated into the main project schedule or Line-of-Balance (LOB) chart.

Another key difference between Multiple Activity Charts and bar charts is the time unit. Multiple Activity Charts typically work in hours or minutes (and occasionally fractions of minutes). Bar-charts typically have a timescale of days and weeks.

Work Measurement Studies-

Work Measurement Study is a general term used to describe the systematic application of industrial engineering techniques to establish the work content and time it should take to complete a task or series of tasks.

Work measurement is a productivity improvement tool. Before improvements can be made, the current productivity level of an organization must be measured. This measurement is then used as a baseline to determine if improvement projects have resulted in genuine improvement.

Work measurement helps to uncover non-value added areas of waste, inconsistency, and non-standardization that exist in the workplace. Work measurement studies uncover ways to make work easier, and to produce products or services more quickly and economically.

Work is measured for four reasons:

à To discover and eliminate lost or ineffective time.

à To establish standard times for performance measurement.

à To measure performance against realistic expectations.

à To set operating goals and objectives.

Work measurement involves the use of engineered labour standards to measure and control the amount of time required to perform a specific task or tasks. While labour standards are most commonly associated with manufacturing or production environments, standards are used in many other types of settings including, but not limited to: service or administrative, warehousing and distribution, retail, janitorial, medical and utilities.

Work Measurement Techniques-

Under the work measurement umbrella there are a number of techniques for collecting the information necessary to develop engineered labour standards.

Time Study is the most widely used work measurement technique that employs a decimal minute stopwatch to record and determine the time required by a qualified and well-trained person working at a normal pace to do a specific task under specified conditions. The result of the time study is the time that a person suited to the job and fully trained in the specified method will need to perform the job if they work at a normal or standard pace.

Predetermined Time Systems are a technique of motion study and time standards development. The motions of the work or task performed are recorded. Each basic motion has a time value associated with it. Once all the motions for the task have been recorded, the time values are totalled and the standard time for the operation is developed.

Standard Time Data (Standard Data) is a generic term given to a collection of time values. Standard data uses work elements from time studies or other work measurement sources making it unnecessary to restudy work elements that have been timed adequately in the past. These element times are extracted from studies and applied to jobs or tasks with the same element(s). Some examples of standard data development include graphs, tables, charts, formulas and spreadsheet programs.

Work Sampling is a random sampling technique (statistical sampling theory) that involves observing the worker(s) at randomly selected times and recording the type of activity that is observed at that instance. Work sampling is most commonly used to collect information for allowance calculation, to determine the distribution of work activities, and to determine the productive and non-productive utilization of workers.

define elementary theory of sets, also explain various methods by giving suitable examples, narrate the utility of “set theory” in an organization

noreply@blogger.com (Manoj Maikhuri) — Mon, 08 Apr 2013 04:32:00 +0000

Most, if not all, of pure mathematics is couched in the language of sets. You may notice that this section contains many definitions and only a few theorems. However, even a definition can contain a lot of mathematical wisdom. It took mathematicians centuries to formulate some fundamental definitions.

Set:

A set is a collection of items considered as a whole. If there are only a few items, the set can be defined by listing them in braces. For example, the set A might be defined as follows:

A = {1, 2, 3}

The items in a set are called elements or members of the set. They are also said to belong to the set or to be in the set, and the set is said to contain them. The symbol ∈ is used to express this relationship --a∈ A means a belongs to A, and a∉ A means a does not belong to A.

Two sets are equal if they contain exactly the same elements. That is, set A is equal to set B if every element of A is also an element of B, and every element of B is also an element of A. The order in which the elements of a set are listed in its definition is irrelevant. For example, the sets {1, 2, 3} and {3, 2, 1} are equal.

An element cannot belong to a set more than once. Therefore, when a set is defined by listing its elements, each element is listed only once.

A set that contains no elements is called the empty set, and is represented by the symbol ∅.

If every element of the set A is also an element of the set B, then A is said to be a subset of B, represented symbolically by A⊆ B, or B is said to include A. Every set is a subset of itself, and the empty set is a subset of every set.

If A⊆ B and there is at least one element of B that is not an element of A, then A is said to be a proper subset of B, represented symbolically by A⊂ B.

A subset is often defined by some property of its elements. For example, let A = {1, 2, 3, 4, 5, 6}, and let B = {2, 4, 6}. Then B could be defined as the set of all elements of A which are even, or in symbols:

B= {x∈ A | x is even}.

Here the symbol | means "such that". The word "all" is understood. In some cases the set A may also be understood.

The intersection of any number of sets is the set of elements that they all have in common. For example, the intersection of {1, 2, 3, 4, 5}, {2, 3, 4, 5, 6, 7, 8, 9} and {3, 5, 7, 9} is {3, 5}. It is clear that the intersection of a collection of sets is a subset of every set in the collection. The intersection of two sets A and B is represented symbolically by A∩B.

The intersection operation has several obvious properties:

· Commutativity: A∩B = B∩A.

· Associativity: (A∩B)∩C = A∩(B∩C).

· A∩B = A if, and only if, A⊆ B.

The union of any number of sets is the set of all of their elements. For example the union of {1, 2, 3, 4, 5}, {2, 3, 4, 5, 6, 7, 8, 9} and {3, 5, 7, 9} is {1, 2, 3, 4, 5, 6, 7, 8, 9}. It is clear that every set in a union is a subset of their union. The union of two sets A and B is represented symbolically by A∪ B.

The union operation has several obvious properties:

· Commutativity: A∪ B = B∪A.

· Associativity: (A∪ B)∪ C = A∪(B∪C).

· A∪B = B if, and only if, A⊆ B.

Two sets are said to be disjoint if they have no elements in common; i.e., A and B are disjoint if A∩B = ∅. Three or more sets are said to be disjoint if every two of them are disjoint.

The notation A-B is used to indicate the set of all elements of A that are not elements of B. This operation has no standard name, but when B is a subset of A, A-B is sometimes said to be the complement of B in A.

The relationships among sets are often represented pictorially by a Venn diagram, in which sets are represented as the interiors of overlapping circles (or other plane figures). Set combinations are represented by areas bounded by the circles, as shown in the following example for two sets:-

Ordered Pairs

An ordered pair is a set of two elements in a specified order. An ordered pair is usually written (a,b) where a is the first element and b is the second element. Two ordered pairs (a,b) and (c,d) are equal if a=c and b=d. Reversing the elements of an ordered pair produces a different ordered pair if the elements are not the same. For example, the ordered pair (1,2) is not equal to the ordered pair (2,1).

For two sets A and B, the cross product A⨯ B is the set of all ordered pairs whose first and second elements are elements of A and B, respectively. That is,

A⨯ B = {(a, b) ∣ a∈ A and b∈ B}

Ordered triples, quadruples, etc. could be defined, but they are seldom needed.

Relations

A relation R on a set A is simply a set of ordered pairs of elements of A, i.e., R ⊆ A⨯ A. Two elements a and b are said to obey the relation if (a,b) is in R. However, for most relations, the set notation is not used. Instead, a symbol such as ~ placed between the elements to indicate that they obey the relation; for example a~b means that (a,b) is in R.

Other symbols often used for relations are

= > < ≥ ≤ ∣ ≠ ⊃ ⊂ ⊇ ⊆ ≡

Most useful relations have some additional properties. A relation ~ on the set A is equivalence if the following hold for every a, b and c in A:

· It is reflexive: a~a.

· It is symmetric: a~b implies that b~a.

· It is transitive: a~b and b~c imply that a~c.

A set of nonempty subsets of a set A is called a partition of A if each element of A belongs to one and only one of the subsets; i.e., if the subsets are disjoint and their union is A. The following theorem establishes a connection between an equivalence relation and a partition.

Theorem: If ~ is an equivalence relation on the set A, then there is partition of the set A such that a~b if, and only if, a and b belong to the same set in the partition. Conversely, if P is a partition of A, then "a and b belong to the same set in P" is an equivalence relation.

Proof. Consider the set P of subsets T_a = {x ∈ A | x~a}. Clearly every a in A belongs to at least one subset in P, namely T_a. Hence the sets in P are nonempty and their union is A. Now let T_a and T_b be two subsets in P. If they have an element c in common, then c~a, c~b and x~b for every x ∈ T_b. By transitivity x~a and x ∈ T_a, too. Similar arguments show that every element of T_a is also an element of T_b. Hence T_a and T_b are equal. If two subsets in P have no element in common, they are disjoint. Hence P is the desired partition.

The converse is trivial.

The sets in the partition associated in this way with an equivalence relation are called its equivalence classes. They are often used to define mathematical systems.

Equivalence relations on two sets A and B can be used to define an equivalence relation on A⨯ B in the obvious way: (a,b) is equivalent to (c,d) if a is equivalent to cand b is equivalent to d.

Order

A partial order on a set A is a relation ≤ with the following properties for every a, b and c in A:

· It is reflexive: a ≤ a.

· It is antisymmetric: a ≤ b and b ≤ a imply that a = b.

· It is transitive: a ≤ b and b ≤ c imply that a ≤ c.

A partial order ≤ on the set A is called a linear order (or a total order) if, for every two elements a and b of A, a ≤ b or b ≤ a (or both, if a = b).

The set of all subsets of a set is partially ordered by inclusion: S ≤ T means S⊆ T. This partial order is usually not a total order because we can find two subsets, such as {1,2,3} and {2,3,4}, such that neither is a subset of the other.

The familiar relation ≤ in arithmetic is a total order.

In working with a partial or total order, it is common to define some associated relations:

· a ≥ b means b ≤ a,

· a < b means a ≤ b and a ≠ b,

· a > b means b ≤ a and b ≠ a.

There is an alternative way to define partial and total orders. A relation < is a partial order if obeys the following two conditions:

· It is transitive: a < b and b < c implies that a < c.

· a < a is always false.

A partial order is a total order if it is also trichotomous: for any two elements a and b, one and only one of the following holds:

· a < b,

· a = b,

· b < a.

The other relations are then defined in terms of <:

· a ≤ b means a < b or a = b.

· a ≥ b means b < a or a = b.

· a > b means b < a.

It can be shown that the two ways of defining partial and total orders are equivalent.

Generally, the names "partial order" and "total order" are applied to the entire set of relations ≤, <, > and ≥ without specifying which is the order relation and which are associated with it.

Operations

A unary operation on a set is a function whose domain is that set. What distinguishes a unary operation from an ordinary function is the notation used, and often its relationships with other functions or operations. For example, the function that carries any real number x to the number -x is a unary operation called negation. The range of the function is often the same set, but this is not required.

A binary operation is a function whose domain is the cross product of two sets (or the cross product of a set with itself). For example, addition and multiplication are two binary operations on the set R⨯ R, where R is the set of real numbers. The image of an ordered pair (x,y) is usually written as x+y for addition and xy for multiplication. Here x and y are called the operands. The former notation is usually used only for addition, or operations very much like addition. The latter notation is used for more general operations.

Unary and binary operations are very common in mathematics; operations with three or more operands are rare, except for extensions of binary operations as noted below. Binary operations on a single set are more common than binary operations on pairs of sets, but both are encountered frequently.

A binary operation is said to be associative if it can be used on three operands without regard to their grouping, i.e., if

(xy)z = x(yz)

(x + y) + z = x + (y + z)

If a binary operation is associative, we can write the result for three operands without parentheses, making it a well-defined operation with three operands:

xyz = (xy)z = x(yz)

x + y + z = (x + y) + z = x + (y + z)

Ordinary addition and multiplication are associative; so are many other binary operations. The composition of functions is an associative binary operation (provided the functions have suitable domains and ranges). In fact, most binary operations are associative.

If a binary operation is associative, it is easy to extend the property to operations on four or more operands:

wxyz = (wxy)z = (w(xy))z = w((xy)z) = w(xyz).

A binary operation is commutative if the order of the operands does not affect the result; i.e., if

xy = yx

x + y = y + x

If a commutative operation is also associative, commutativity can easily be extended to operations on three or more operands:

xyz = (xy)z = (yx)z = yxz = y(xz) = (yx)z = yxz, etc.

Binary operations which are commutative but not associative are very rare. Binary operations which are associative but not commutative are fairly common. Composition of functions is one example; a demonstration of that fact will be given later.

Now consider a binary operation on A⨯ B with its range in C (which need not be different sets). Suppose there are equivalence operations on these sets (which also need not be different), and the binary operation preserves equivalence, i.e., the operation when applied to equivalent operands gives equivalent results, or a ~ b and c ~ d imply that ac ~ bd. Then, just as a function of one variable was extended to a function on equivalence classes, an operation on two variables can be extended similarly. If a and b are any elements of the equivalence classes P and Q, respectively, then PQ is defined to be the equivalence class containing ab. The new operation inherits many properties of the old one, including associatively and commutatively.

Utility of “set theory” in an organisation

A company consists of sets of resources like personnel, machines, material stocks and cash reserves. The relationship between these sets and between the subsets of each set is used to equate assets of one kind with another. The subsets of highly skilled production workers, within the sets of all production workers, are critical subset that determines the productivity or other personnel. Certain subsets of company products are highly profitable or certain material may be subject to deterioration and must be stocked in greater quantities than others. Thus the concept of sets is very useful in business management.

Enumerate some important development of statistical theory; also explain merits and limitations of statistics.

noreply@blogger.com (Manoj Maikhuri) — Thu, 04 Apr 2013 04:13:00 +0000

Statistical data analysis is generally considered one of the major scientific achievements of the twentieth century. In hard sciences such as seismology or meteorology, a theory based on physical law is not considered successful unless it predicts better than purely statistical arguments. In other disciplines such as econometrics, statistical finance, genomics, statistical analysis is essential to identifying phenomena. It is perhaps much less recognized that the constitution in the 20^th century of Statistics as an independent scientific discipline is one of the reasons for this intellectual success as well as a consequence.

Probably the best demonstrations of the vitality of statistical ideas are continuing attempts at their appropriation. For example, a recent Nobel Prize in economics essentially recognized research in statistical time series analysis that had application to econometrics. The past century saw the emergence of what are called the mathematical sciences. These include theoretical physics, quantum chemistry, mathematical statistics, and more.
We are now witnessing the emergence of the statistical sciences: disciplines that rely on statistical theory to express their specific ideas. Examples of statistical sciences include biostatistics, econometrics, statistical finance, genomics, signal processing, machine learning, and more. Supporting the growth of the statistical sciences has been the technological revolution in computing.

Until the late 1950's, writers on competing statistical theories thought in terms of virtual data governed by probability models involving relatively few parameters. That same decade found logical paradoxes in the statistical theory of the time which obliged more careful rethinking. By 1970, sophisticated rigorous development of statistical theory had resolved these paradoxes. From the 1960's onward, computing technology and refined concepts of algorithm provided a new environment in which to extend and reconsider statistical ideas developed with probability technology. Case studies and experiments with artificial data increasingly offered non-probabilistic ways of understanding the performance of statistical procedures.

The fundamental distinctions among data, probability model, pseudo-random numbers, and algorithm returned to prominence. It became clear once again that data is often not certifiably random. The bonds that had for many decades linked statistical theory closely to probability models began to loosen in favour of broader views. Use of heuristic data-analytic algorithms not supported by theoretical analysis grew rapidly. Our phrase “the new statistics” refers to the present, much enlarged, concept of statistical methodology.

The “core of statistics” is the subset of statistical activity that is focused inward, on the subject itself, rather than outward, towards the needs of statistics in particular scientific domains. Of necessity, statistical research that seeks to serve the statistical sciences draws on core knowledge for tools as well as for an understanding of the limitations of the tools. Work in the statistical sciences can be collaboration between a statistician and a scientist in the substantive field. However, with the explosion of data needing attention, such collaborations alone cannot possibly fill the need. The rapidly growing needs of the statistical sciences provide raw material for future core research in statistics and motivates the development of trustworthy, user-friendly statistical methodology. Indeed, statistics fluctuates between import and export mode: importing raw data-analytic ideas inspired by the technology and problems of the moment and exporting refined data-analytic procedures, whose characteristics are understood theoretically and experimentally, to the community of quantitative scientists. The phrase "core of statistics" refers precisely to the intellectual basis for the export mode.

The authors of the 2002/2004 Report on the Future of Statistics mentioned in the Overview, the current lag in developing core statistical theory to meet the data-analytic needs of the statistical sciences. The previous paragraph draws on ideas in this report. A few examples where statistical theory needs major development include:

Accepted standards for experimental exploration of new statistical methods
Methods for data-mining;
Automatic methods for discovering new patterns in extremely large data-sets;
Automatic methods for classifying and recognizing patterns;
Methods for verifying data integrity;
Regularization methods for difficult ill-posed problems such as mass tomography based on noisy data.

Merits of Statistics:

Presenting facts in a definite form
Simplifying mass of figure- condensation into few significant figures
Facilitating comparison
Helping in formulating and testing of hypothesis and developing new theories.
Helping in predictions.
Helping in formulation of suitable policies.

Limitations of statistics:

Statistics with all its wide application in every sphere of human activity has its own limitations. Some of them are given below.

1. Statistics is not suitable to the study of qualitative phenomenon: Since statistics is basically a science and deals with a set of numerical data, it is applicable to the study of only these subjects of enquiry, which can be expressed in terms of quantitative measurements. As a matter of fact, qualitative phenomenon like honesty, poverty, beauty, intelligence etc, cannot be expressed numerically and any statistical analysis cannot be directly applied on these qualitative phenomenons. Nevertheless, statistical techniques may be applied indirectly by first reducing the qualitative expressions to accurate quantitative terms. For example, the intelligence of a group of students can be studied on the basis of their marks in a particular examination.

2. Statistics does not study individuals: Statistics does not give any specific importance to the individual items; in fact it deals with an aggregate of objects. Individual items, when they are taken individually do not constitute any statistical data and do not serve any purpose for any statistical enquiry.

3. Statistical laws are not exact: It is well known that mathematical and physical sciences are exact. But statistical laws are not exact and statistical laws are only approximations. Statistical conclusions are not universally true. They are true only on an average.

4. Statistics table may be misused: Statistics must be used only by experts; otherwise, statistical methods are the most dangerous tools on the hands of the inexpert. The use of statistical tools by the inexperienced and untraced persons might lead to wrong conclusions. Statistics can be easily misused by quoting wrong figures of data. As King says aptly ‘statistics are like clay of which one can make a God or Devil as one pleases’.

5. Statistics is only, one of the methods of studying a problem: Statistical methods do not provide complete solution of the problems because problems are to be studied taking the background of the countries culture, philosophy or religion into consideration. Thus the statistical study should be supplemented by other evidences.

What do you understand by word “Statistics”? give out its definitions (minimum by 4 Authors) as explained by various distinguished authors.

noreply@blogger.com (Manoj Maikhuri) — Tue, 02 Apr 2013 04:19:00 +0000

Introduction:

In the modern world of computers and information technology, the importance of statistics is very well recognised by all the disciplines. Statistics has originated as a science of statehood and found applications slowly and steadily in Agriculture, Economics, Commerce, Biology, Medicine, Industry, planning, education and so on. As on date there is no other human walk of life where, statistics cannot be applied.

Origin and Growth of Statistics:

The word ‘Statistics’ and ‘Statistical’ are all derived from the Latin word Status, means a political state. The theory of statistics as a distinct branch of scientific method is of comparatively recent growth. Research particularly into the mathematical theory of statistics is rapidly proceeding and fresh discoveries are being made all over the world.

Meaning of Statistics:

Statistics is concerned with scientific methods for collecting, organising, summarising, presenting and analysing data as well as deriving valid conclusions and making reasonable decisions on the basis of this analysis. Statistics is concerned with the systematic collection of numerical data and its interpretation. The word ‘ statistic’ is used to refer to

i) Numerical facts, such as the number of people living in particular area.

ii) The study of ways of collecting, analysing and interpreting the facts.

Definitions:

Statistics is defined differently by different authors over a period of time. In the olden days statistics was confined to only state affairs but in modern days it embraces almost every sphere of human activity. Therefore a number of old definitions, which was confined to narrow field of enquiry, were replaced by more definitions, which are much more comprehensive and exhaustive. Secondly, statistics has been defined in two different ways– Statistical data and statistical methods. The following are some of the definitions of statistics as numerical data.

i) Statistics are the classified facts representing the conditions of people in a state. In particular they are the facts, which can be stated in numbers or in tables of numbers or in any tabular or classified arrangement.

ii) Statistics are measurements, enumerations or estimates of natural phenomenon usually systematically arranged, analysed and presented as to exhibit important interrelationships among them.

Definitions by A.L. Bowley:

Statistics are numerical statement of facts in any department of enquiry placed in relation to each other. - A. L. Bowley

Statistics may be called the science of counting in one of the departments due to Bowley, obviously this is an incomplete definition as it takes into account only the aspect of collection and ignores other aspects such as analysis, presentation and interpretation.

Bowley gives another definition for statistics, which states ‘statistics may be rightly called the scheme of averages’. This definition is also incomplete, as averages play an important role in understanding and comparing data and statistics provide more measures.

Definition by Croxton and Cowden:

Statistics may be defined as the science of collection, presentation analysis and interpretation of numerical data from the logical analysis. It is clear that the definition of statistics by Croxton and Cowden is the most scientific and realistic one.

According to this definition there are four stages:

1. Collection of Data: It is the first step and this is the foundation upon which the entire data set. Careful planning is essential before collecting the data. There are different methods of collection of data such as census, sampling, primary, secondary, etc., and the investigator should make use of correct method.

2. Presentation of data: The mass data collected should be presented in a suitable, concise form for further analysis. The collected data may be presented in the form of tabular or diagrammatic or graphic form.

3. Analysis of data: The data presented should be carefully analysed for making inference from the presented data such as measures of central tendencies, dispersion, correlation, regression etc.,

4. Interpretation of data: The final step is drawing conclusion from the data collected. A valid conclusion must be drawn on the basis of analysis. A high degree of skill and experience is necessary for the interpretation.

Definition by Horace Secrist:

Statistics may be defined as the aggregate of facts affected to a marked extent by multiplicity of causes, numerically expressed, enumerated or estimated according to a reasonable standard of accuracy, collected in a systematic manner, for a predetermined purpose and placed in relation to each other.

The above definition seems to be the most comprehensive and exhaustive.

Functions of Statistics:

There are many functions of statistics. Let us consider the following five important functions.

1) Condensation: Generally speaking by the word ‘to condense’, we mean to reduce or to lessen. Condensation is mainly applied at embracing the understanding of a huge mass of data by providing only few observations. If in a particular class in Chennai School, only marks in an examination are given, no purpose will be served. Instead if we are given the average mark in that particular examination, definitely it serves the better purpose. Similarly the range of marks is also another measure of the data. Thus, Statistical measures help to reduce the complexity of the data and consequently to understand any huge mass of data.

2) Comparison: Classification and tabulation are the two methods that are used to condense the data. They help us to compare data collected from different sources. Grand totals, measures of central tendency measures of dispersion, graphs and diagrams, coefficient of correlation etc provide ample scope for comparison.

If we have one group of data, we can compare within itself. If the rice production (in Tonnes) in Tanjore district is known, then we can compare one region with another region within the district. Or if the rice production (in Tonnes) of two different districts within Tamilnadu is known, then also a comparative study can be made. As statistics is an aggregate of facts and figures, comparison is always possible and in fact comparison helps us to understand the data in a better way.

3) Forecasting: By the word forecasting, we mean to predict or to estimate before hand. Given the data of the last ten years connected to rainfall of a particular district in Tamilnadu, it is possible to predict or forecast the rainfall for the near future. In business also forecasting plays a dominant role in connection with production, sales, profits etc. The analysis of time series and regression analysis plays an important role in forecasting.

4) Estimation:

One of the main objectives of statistics is drawn inference about a population from the analysis for the sample drawn from that population. The four major branches of statistical inference are

i) Estimation theory

ii) Tests of Hypothesis

iii) Non Parametric tests

iv) Sequential analysis

In estimation theory, we estimate the unknown value of the population parameter based on the sample observations. Suppose we are given a sample of heights of hundred students in a school, based upon the heights of these 100 students, it is possible to estimate the average height of all students in that school.

5) Tests of Hypothesis: A statistical hypothesis is some statement about the probability distribution, characterising a population on the basis of the information available from the sample observations. In the formulation and testing of hypothesis, statistical methods are extremely useful. Whether crop yield has increased because of the use of new fertilizer or whether the new medicine is effective in eliminating a particular disease are some examples of statements of hypothesis and these are tested by proper statistical tools.

Explain how Program Evaluation and Review Technique (PERT) is used in project management, support your answer with suitable diagram

noreply@blogger.com (Manoj Maikhuri) — Sat, 23 Mar 2013 04:37:00 +0000

Program (Project) Evaluation and Review Technique (PERT) is a project management tool used to schedule, organize, and coordinate tasks within a project. It is basically a method to analyze the tasks involved in completing a given project, especially the time needed to complete each task, and to identify the minimum time needed to complete the total project. PERT planning involves the following steps:

· Identify the specific activities and milestones.

· Determine the proper sequence of the activities.

· Construct a network diagram.

· Estimate the time required for each activity.

· Determine the critical path.

· Update the PERT chart as the project progresses.

The main objective of PERT is to facilitate decision making and to reduce both the time and cost required to complete a project. PERT is intended for very large-scale, one-time, non-routine, complex projects with a high degree of inter-task dependency, projects which require a series of activities, some of which must be performed sequentially and others that can be performed in parallel with other activities.

DESCRIPTION

PERT planning involves the following steps that are described below.

1. Identify the specific activities and milestones.

The activities are the tasks required to complete a project. The milestones are the events marking the beginning and the end of one or more activities. It is helpful to list the tasks in a table that in later steps can be expanded to include information on sequence and duration.

2. Determine the proper sequence of the activities.

This step may be combined with the activity identification step since the activity sequence is evident for some tasks. Other tasks may require more analysis to determine the exact order in which they must be performed.

3. Construct a network diagram.

Using the activity sequence information, a network diagram can be drawn showing the sequence of the serial and parallel activities. Each activity represents a node in the network, and the arrows represent the relation between activities. Software packages simplify this step by automatically converting tabular activity information into a network diagram.

4. Estimate the time required for each activity.

Weeks are a commonly used unit of time for activity completion, but any consistent unit of time can be used. A distinguishing feature of PERT is its ability to deal with uncertainty in activity completion time. For each activity, the model usually includes three time estimates:

o Optimistic time – generally the shortest time in which the activity can be completed. It is common practice to specify optimistic time to be three standards deviations from the mean so that there is a approximately a 1% chance that the activity will be completed within the optimistic time.

o Most likely time – the completion time having the highest probability. Note that this time is different from the expected time.

o Pessimistic time – the longest time that an activity might require. Three standard deviations from the mean is commonly used for the pessimistic time.

PERT assumes a beta probability distribution for the time estimates. For a beta distribution, the expected time for each activity can be approximated using the following weighted average:

o Expected time = ( Optimistic + 4 x Most likely + Pessimistic ) / 6

o This expected time may be displayed on the network diagram.

o To calculate the variance for each activity completion time, if three standard deviation times were selected for the optimistic and pessimistic times, then there are six standard deviations between them, so the variance is given by:

[(Pessimistic - Optimistic) / 6]

5. Determine the critical path.

The critical path is determined by adding the times for the activities in each sequence and determining the longest path in the project. The critical path determines the total calendar time required for the project. If activities outside the critical path speed up oe slow down (within limits), the total project time does not change. The amount of time that a non – critical path activity can be delayed without the project is referred to as a slack time.

If the critical path is not immediately obvious, it may be helpful to determine the following four quantities foe each activity:

i. ES – Earliest Start time

ii. EF - Earliest Finish time

iii. LS – Latest Start time

iv. LF - Latest Finish time

These times are calculated using the expected time for the relevant activities. The earliest start and finish times of each activity are determined by working forward through the network and determining the earliest time at which an activity can start and finish considering its predecessors activities. The latest start and finish times are the latest times that an activity can start and finish without delaying the project. LS and LF are found by working backward through the network. The difference in the latest and earliest finish of each activity is that activity’s slack. The critical path then is the path through the network in which none of the activities have slack.

The variance in the project completion time can be calculated by summing the variances in the completion times of the activities in the critical path. Given this variance, one can calculate the probability that the project will be completed by the certain date assuming a normal probability distribution for the critical path. The normal distribution assumption holds if the number of activities in the path is large enough for the central limit theorem to be applied.

Since the critical path determines the completion date of the project, the project can be accelerated by adding the resources required to decrease the time for the activities in the critical path. Such a shortening of the project sometimes is referred to as project crashing.

6. Update the PERT chart as the project progresses.

Make adjustments in the PERT chart as the project progresses. As the project unfolds, the estimated times can be replaced with actual times. In cases where there are delays, additional resources may be needed to stay on schedule and the PERT chart may be modified to reflect the new situation.

BENEFITS

· PERT is useful because it provides the following information:

· Expected project completion time;

· Probability of completion before a specified date;

· The critical path activities that directly impact the completion time;

· The activities that have slack time and that can be lend resources to critical path activities;

· Activity start and end date.

PREREQUISITES

· Personnel should already have a good understanding of formal project management terminology, tools, and techniques

· PERT form template of equivalent tool (e.g. software)

· Create of project plan

· Choose the most appropriate scheduling method

· Select and organize a team to perform project tasks.

LIMITATIONS:

The following are some of PERT's weaknesses:

· The activity time estimates are somewhat subjective and depend on judgement. In cases where there is little experience in performing an activity, the numbers may be only a guess. In other cases, if the person or group performing the activity estimates the time there may be bias in the estimate.

· Even if the activity times are well-estimated, PERT assumes a beta distribution for these time estimates, but the actual distribution may be different.

Even if the beta distribution assumption holds, PERT assumes that the probability distribution of the project completion time is the same as that of the critical path. Because other paths can become the critical path if their associated activities are delayed, PERT consistently underestimates the expected project completion time.

Example: In the following example, the Project manager knows the succession of the project activities and the optimistic, pessimistic and most likely time (in weeks) for the following activities:

The associated network is:

The earliest start time, earliest finish time, latest start time and latest finish time for each activity are calculated in the following table:

Which are the five Principles that have been suggested by psychologist Blum? Can you add a few more from your side?

noreply@blogger.com (Manoj Maikhuri) — Fri, 22 Mar 2013 11:07:00 +0000

Industrial psychology, as the name indicates, is that branch of psychology which studies human behaviour industrial circumstances. Psychology is a normative science of behaviour which can be observed in the social, economic, educational and other spheres. In the modern period, the different branches of psychology have developed during the study of human behaviour in these various fields.

This is an age of industrial progress, in which each progressive nation is expanding the scope of its industries. But industries cannot be made to function without human being, because individuals are required in the capacity of managers, administrators, programmers, etc. And, wherever there are human beings, their behaviour will be influenced by their relationships. If, in the industrial field, human relations deteriorate, then the result in strikes and lockouts which have a widespread impact upon the economy of the nation. Since the future of every nation depends its industrial development and progress, this particular aim of industrial psychology to study the principles governing behaviour in the industrial field and to undertake research.

According to M. L. Blum, the psychologist, “industrial psychology is simply the application or extension of psychological facts and principles to the problems concerning human relations in business and industry”.

Industrial sociology also studies problems concerning human relation in business and industry through from the sociological point of view. This common subject matter naturally creates a close relationship between industrial sociology and industrial psychology. Even when working in an industry, the individuals are never alone because he constantly comes in to contact with other workers, supervisors, managers and industrialists. In business, the worker has to deal with all kinds of people. For this reason it also becomes necessary to study the social foundations of industrial psychology. Efforts are being made to bring industrial organization to s scientific level. The more progressive countries try to improve the social life of the worker by organizing various kinds of social programmers. The following points need to be considered in a study of the relationship of industrial sociology and industrial psychology:

1. Worker and social progress.

2. Workers’ department and welfare schemes

3. Industrial management

Principles that have been suggested by Psychologist M. L. Blum

The psychologist M. L. Blum has suggested the following principles for effective supervision

Don’t be an autocrat: Autocratic leadership is self-defeating because the workers feel alienated and do not obey the leader willingly.
Listen carefully: The leader should be a good listener. He must listen carefully to everything told to him by subordinates. He must accord full opportunity to the worker to present his case, only after it should the leader express his opinion.
Don’t decide hurriedly: it is rash to form an opinion about some person hurriedly after a casual glance or brief hearing as most of the complains are not genuine. But some are certainly so and if these are not removed the morale in the company would suffer.
Don’t argue with subordinates: under no circumstances supervisor should indulge in argument with his subordinate, because if he fails to persuade or convince by his arguments, he is liable to issue orders to vindicate his point of view. This creates frustration and sense of insecurity among the workers. A good leader should not argue but listen carefully and issue orders only after careful listening.

Don’t criticism publicly: If a worker is rebuked publicly he feels insulted. The workers are rash and they tend to retaliate if they are wounded emotionally. Therefore, a good leader avoids discussing the faults of workers in public. He points them out to him in private and in a direct manner.

What is an industrial sociology? And what is its scope; also narrate value of industrial sociology in India.

noreply@blogger.com (Manoj Maikhuri) — Mon, 18 Mar 2013 04:34:00 +0000

Industrial Sociology, a term came into use in the middle of twentieth century owing to the famous experiments conducted by George Elton Mayo and his associates during late twenties and early thirties at Haw throne Works in Chicago.

It is the application of sociological approach to the reality and problems of industry. The importance attached to the subject can be gauged to the fact that India has committed to the industrial development as a necessary means to solve its economic and social problems. Here, we have to understand key terms: ‘industry’ and ‘sociology.’

Industry- Industry may be understood as, ‘the application of complex and sophisticated methods for the production of economic goods and services’.

These complex methods, implying use of machinery, have been devised to improve quality and quantity of production.

To shine, enter into industry. From food gatherer to food producer, man has always been industrial, i.e., hard-working. Coming together and start living in a group, man always tried to develop. He has always used tools to get food and satisfy his needs. To get his daily breads, pre-literate man used digging stick, hoe or bow and arrow. Anthropology and History show that man has always been not only user of tools but also maker of tools, i.e., homo Faber. Adjustment, one of the characteristics of man, makes man move from means to ends, to achieve his objectives. That is why man is also called homosapiens (intelligent man) and so homo industrialise (industrial man).

From French Industrie or Latin industria, here group of diligent and hard-working men came together and formed a unit to carry economic activity concerned with the processing of raw materials and manufacture of goods in factories.

Sociology- The Science, dealing with the behavior of man living in group, which developed over a period of time, is known as Sociology or Science of society. Society itself consists in the web of social relationships with the combinations and complexities arising from them, as clique (a small group of people who spend time together and do not allow others to join them), groups, associations, institutions, systems, etc.

The root or beginning of society is the social relationship which essentially develops on mutual awareness, followed by reciprocal interrelations and interactions. Thus, we may say that strangers do not make society. Like, two students come to college from different areas, different backgrounds and never seen each other in past. Though physically present in college, they have not found occasion to start talking. But when they come close to each other on introduction, the social relationship begins to emerge and may even become more complex when they begin to deal with each other or talk business.

Generally, sociology is restricted to the field of study of social institutions or social systems as the family or state, the village or factory etc. The objectives are undoubtedly legitimate, but there does not seem to be any cogent reason to exclude other social realities, such as group or social relationship itself.

Any reality, especially a complex reality like industry, can be studied from various points of view, such as technological, physical, psychological etc. But we must give emphasis to the sociological aspect or social element that manifests itself in industry.

Definitions

1) “Industrial Sociology is concerned with industry as a social system, including those factors (technical, economic, political) which affect the structure, the functions and the changes in that system.”- Smith J.H., Industrial Sociology, UNESCO Publication

2) “Industrial Sociology is a substantive area of general sociology which might more accurately be termed the sociology of work organisations or sociology of economy.”- D.C. Miller and W.H. Form, Industrial Sociology, New York

3) “Lupton has been more specific and advanced the study of the social system of the factory and of the influences external to the factory which affects that system.”- Ibid

4) “Industrial Sociologists centre their interests upon the social organisations of the work place, including the pattern of interaction among people who are responding to one another in terms of their roles in work organisations or whose behaviour is being affected by those roles.”- Charles B. Spaulding

5) “Industrial Sociology is concerned with how the economic subsystem is related to other sub-system, how the sub-system is structured in terms of particular work organisation and roles and how persons fit into these roles.”- Parker S.R., Brown R.K. and others.

Scope of Industrial Sociology

Every science has its own areas of inquiry. It becomes difficult for any one to study a science systematically unless its boundaries are demarcated and scope determined precisely. Unfortunately, there is no consensus on the part of Industrial sociologist with regard to the scope of Industrial sociology. V. F. Calberton comments. "Since sociology is so elastic a science, it is difficult to determine just where its boundaries began and ends, where Industrial sociology becomes social psychology and where social psychology becomes Industrial sociology, or where economic theory becomes sociological doctrine or biological theory becomes sociological theory something, which is impossible to decide.

Significances / Value of industrial sociology in India

Industrial Sociology has acquired values in progressive society where industrial revolution played great role which was necessitated to cater the growing demands of ever increasing population.

Following benefits increase the values of Industrial Sociology:

1. Use of Scientific Methods

Industrial Sociology acquires great value in view of scientific methods adapted to study. To understand it by establishing cause-effect relationship, scientific knowledge helps. All round progress of mankind depends upon moving up the ladder of Maslow’s hierarchy of needs, i.e., food (bottom-most need).

With the onset of industrial revolution, development of industries took the center stage. Industrial development in any nation became a key factor in deciding the progress of that nation. As science helped in industrial development, the systematic knowledge acquired using scientific methods human behavior became very handy. Thus industrial sociology as a subject of study gained currency. Various institutions dealing with industrial society, its social control, social dynamics, social change, problems and their remedies came up and thereby widened the scope of industrial sociology. It would not be exaggeration to say that without knowledge of industrial sociology industrial progress could not have been achieved. The importance of industrial sociology made the premise of setting up Tata Institute of Social Sciences in Mumbai.

2. Understanding the Social Aspect of Industry

The orderly movement of things based on certain rules takes any work to logical conclusion. That is the importance of science and that is why it is called specialised knowledge. To gauge the development of any nation, the position of its industries plays a great role. The contribution of industries to the well-being of citizens of its country and humanity at large has become now predominant in the index of development of any nation. That is why Corporate Social Responsibility (CSR) has become a buzzword in developing country like India. Industrial Sociology has added new dimension to the knowledge of humanity. The workmen, who do work, execute the design that is taken from drawing board to floor, are vital link between theory and practice. So called labour class, workmen are to be trained, taught and toughened so that they become finest craftsmen. The social strata, importance of roles and problems of workmen are to be addressed in such a way that there ever remain cordial industrial relations and industrial peace should bring development in industries.

3. Solutions from Industrial Society to the Problems of Industry

Based on knowledge and experience people are employed in industries leading to formation of various layers taking care of different job roles and responsibilities. Industrial Revolution gave birth to industries which were organised to achieve its objectives. Job roles decided expertise, division of labour and degree of responsibility and thus created vertical and horizontal set up in the organisation. Occupation hazards are associated with industries which require matching counter balance in the form of money, perks, glamour and other welfare measures.

As workmen, supervisors, executives, policymakers are associated with any industry, a stratum is formed which gives feeling of differentiation, status-consciousness, inferiority-superiority complex etc.

4. Industrial Relations

Industrial relations play vital role in development of industries in any country. The management policies, its objective, long-term and short-term goals decide its working environment. In-short, we can say the mission and vision of any company in theory and practice speak about the company. The relationship between workman and management has to be harmonious for industrial rest and once that is there, industrial progress is bound to happen. Production has to multiply. The production coupled with quality is the need for growth of any industry. Industrial Sociology helps in achievement of this objective. Important factors playing great roles in good industrial relations are rates of wages, working conditions, welfare measures, recruitment policy, job-security etc. Welfare measures are widely covered under the heading of food, cloth and house. Industrial Sociology is gaining importance due to its role played in taking care of all these discussed matters. Mutual trust, sense of belonging, feeling of ownership, team spirit are knitting social industrial fabric which ultimately result into higher quality production and give sustenance and thus growth is achieved. Initial steps of ladder of hierarchy of needs of Maslow viz. food, safety and security are very important to be given top-most priority to make workmen take their duty seriously.

5. Division of Labour

Every one cannot do everything. In India, caste system worked well earlier. In fact, people were easily adopting family profession. However, with good progress in education people started coming out of family business. The sharpening of mind, learning skills of other’s profession and zeal and zest make people go for the occupation which make them happy.

Thus caste system, which earlier caused impediment in economic progress, is losing its effect. Not by design but by default, industrial progress caused mingling particularly in industrial society. Thus caste-system is being eliminated and division of labour is made based on certain other factors viz. innate ability, training, choice etc. Now scientific division of labour in industry demands good knowledge as per profession. Here, role of industrial sociology comes to play.

6. Economic Planning

After India gained freedom, the progress of nation was to be carried out on solid foundations and thus it adopted Five-Year Plan growth approach. Called temples of modern India by first Prime Minister of India Jawaharlal Nehru, industrial growth was aimed in five-year plan to solve many problems arisen mainly out of population. Illiteracy, low level of wages, corruption, lack of trained people and professional approach are some of the problems which require intervention of industrial sociologists.

7. Industrial Automation

Industrial revolution came with onset of mechanisation. The increased requirements of people necessitated invention of steam engine to mechanise processes such as cloth weaving in England. More automation means less manual work and in turn less employment. It gave more profits to industrialists and so more automation was opposed by workmen’s unions. Here role of industrial sociologist brings solution to the dispute.

8. Industrial Management

Industrial management requires scientific approach to take care of turnover, sustenance of activity, quality and other variable factors. Human approach is given in industrial management where all elements of production are most efficiently employed without any exploitation.

9. Integration of Family

The unit of any society is family which requires special attention. Any disturbance caused due to any social evil such as alcoholism, insufficient housing, bad recreation, uncontrolled media, and print as well as electronic would disorganise industrial society in particular. Disintegration of family is bound to happen. Industrial Sociology gives insight leading to solutions of many problems arisen out of progress in industries.

Enumerate the differences between an Estimate and budget. Also describe the likely pitfalls which can impede the pricing function

noreply@blogger.com (Manoj Maikhuri) — Wed, 06 Mar 2013 11:23:00 +0000

Estimating Costs and Determining Budgets

In general we understand how to create the project human resource plan. To get resources you need to spend money (Cost) and you can’t spend an infinite amount. Every project has certain limits on the amount of money you can spend (Budget). Here I am going to explain about estimating the costs and determining the budget involved in a project.

Difference between Cost & Budget:

Many people confuse the terms cost & budget and even use them interchangeably. Unfortunately they are both not the same. So, the first thing we need, understand clearly the difference between cost and budget.

Cost is the value of the inputs that have been (or will be) used up to perform a task or to produce an item: product, service, or result. This value is usually measured in units of money. For example, you paid two programmers Rs. 5000/- each for developing a software program, and you paid Rs.1000/- to a tester to test the program. So, the cost for the task of developing and testing the software program is Rs. 11,000/-. You can add the costs of components of a system, and the sum will represent the cost of the system, but it’s still a cost and not a budget.

Budget is an aggregated cost with a timeline. You aggregate the costs of all the resources needed to perform the project and put a timeline on it: the availability of funds over time. That is called a budget. Look at the image below:

Cost management consists of estimating project costs, determining budget from the cost estimates, and controlling the cost while the project is being executed. This is just a high level depiction of cost management and we will cover the details now.

Estimating Project Costs

Estimating project cost means developing an estimate for the monetary resources needed to complete the project work; that is, activities. These estimates are based on the information available at a given time. The estimates in the beginning are less accurate; for example, their accuracy may be only as good as + or - 50 percent. For example, if you say the cost will be $50,000, it could be anywhere between $25,000 and $75,000. As the project moves along and more information becomes available, the cost estimates can be improved to get better estimates.

The standard process used to estimate costs is called the Estimate Costs process. Look at the image below:

Estimating project cost means estimating the costs required to complete the project scope by executing schedule activities. Therefore, you need the scope baseline and the schedule baseline for estimating costs. The lists of items used as input to this process are:

• Human resource plan - The information in the human resource plan useful for estimating costs includes the list of roles and responsibilities, personnel rates, and recognitions and rewards.

• Project schedule - An approved project schedule will give you the information about the resources needed to complete the project work. This information is crucial to make cost estimates. As you learned in the previous chapter, activity resources are estimated by performing the Estimate Activity Resources process. Therefore, the Estimate Costs process should be closely coordinated with the Estimate Activity Resources process, which in turn depends on the Estimate Activity Durations process because activity duration is determined for the given resources.

• Scope baseline - All three components of the scope baseline; scope statement, WBS, and WBS dictionary are useful in estimating the project cost. The scope statement will provide the cost-relevant information, such as project and product acceptance criteria, assumptions and constraints, product description, key deliverable, and project boundaries around the scope.

• Risk register - Both kinds of risks—threats and opportunities have an impact on the cost in the form of risk mitigation costs and revenues or savings from the opportunities.

• Enterprise environmental factors - Enterprise environmental factors relevant to estimating costs include market conditions and published commercial information that will provide the cost of resources, including human resources, materials, and equipment. This will also provide the information related to the availability of products and services and their cost and rates. Supply and demand conditions can also influence the project cost.

• Organizational process assets - This includes the organization’s policies regarding cost estimates, cost estimating templates, and information from previous projects, including lessons learned.

Determining Project Budget

Determining the project budget is the process of aggregating the cost estimates for all project activities and assigning a timeline to them. Cost aggregation is the technique used to calculate the cost of a whole by summing up the costs of the parts of which the whole is made. You can use the bottom-up estimation technique to aggregate the costs of all the components and activities to calculate the total cost of the project. The timeline assigned to this cost will be important to reconcile the expenditure with the funding limits.

The reconciliation may require rescheduling some activities.

The budget is determined by using the Determine Budget process. Look at the picture below:

Most of the items in the input to this process are already described in this chapter. Organizational process assets may include organizational policies and tools for determining the budget. The reserve analysis at budget level includes management reserve in addition to contingency reserve, and you must understand the difference between the two. Contingency reserves are the funds that can be used to deal with the unplanned events that can potentially transpire in case one or more identified risks occur, whereas management reserves are the funds that can be used in case of yet unplanned but future changes in some aspects of the project, such as the project scope.

The approved budget that includes the aggregated cost with timeline is called the cost baseline. The cost performance of the project is monitored, measured, and controlled against this baseline. This is why it’s also called the cost performance baseline. Funding requirements for the project are derived from the cost baseline and the reserve analysis.

Do not leave out the cost of the internal employees of the organization who will work on the project. They are not free, for two reasons: The organization pays for them, and they do not have infinite numbers of hours to put into the project. Their cost to the project will be determined just like any other project role based on the hours of work they will put into the project.

In the process of determining the budget, you may need to update the project schedule, cost estimates, and the risk register.

Your organization may not have the resources to complete all parts of the project. For those parts of the project, you will need to use what is called procurement.

Factors which change costs over time

Once implementation begins, a project’s costs rarely remain static. As further information becomes available the costs may be further defined. Yet, even when a cost has become firmly fixed, there are numerous factors that can lead to the cost increasing. Delays are a major factor. Whatever the reason, delays almost invariably increase budget costs. Many events may have contributed to the delay – some which could have been foreseen and others which could not.

In the context of program funding, time and cost over-runs have obvious implications for the number of projects that can be funded within a program period, and for the scale of the outputs and impacts generated. These lead to projects over-running either in time or costs. As indicated above, delays generally translate into higher project costs.

Poor Project Management: The role of the project manager or project management team is probably the most important element in containing the costs of a project. It is often true that a poor project with a good project manager will be completed satisfactorily. But even a good project, if combined with poor project management, will almost always face serious difficulties.

A poor project management structure will have an impact at all stages of the process leading to:

• A lack of planning and coordination;

• Poor communication between members of the project team and the project sponsor;

• Failure to identify problems and institute necessary design and programming changes;

• A lack of control over time and cost inputs.

Major or minor changes in requirement: A change in a project’s design can arise for a number of reasons. It may be that the project sponsor wants additional elements to be included in the project or changes to existing ones. Usually, these design changes require additional time inputs from experts as well as the additional time and cost inputs from the team members.

Funding Problems: Overall lack of finance to complete a project or delays in the payment for services by the project sponsor can lead to significant problems arise. If the costs of a project have increased significantly beyond the original estimate, then work on the project may have to stop or be delayed until additional funds can be found.

Funding problems can also arise if funds allocated to one project have been diverted to other projects within a program of development.

Other Factors: In addition to all the categories listed above, experience shows that problems also arise from premeditated under-estimation of initial costs simply in order to obtain initial approval for a project. This can lead to major projects being approved, and started, in the knowledge that actual costs will be very much higher than the “agreed” estimate. Once started, a high profile project is often difficult to stop. So, when the true costs do become apparent, it is difficult for authorities to refuse the additional funding required to complete the project.

What do you understand by Quality & Cost economics, mention categories of Quality Cost as identified by American Society for Quality Control (ASQC)

noreply@blogger.com (Manoj Maikhuri) — Sat, 18 Feb 2012 10:17:00 +0000

Quality & Cost economics

The cost of quality; it is a widely used term and widely misunderstood.

The cost of quality is not the price of creating a quality product or service. It is the cost of NOT creating a a quality product or service.

Every time work is redone, the cost of quality increases. Obvious examples included:

The reworking of a manufactured item
The retesting of an assembly
The rebuilding of a tool
The correction of a blank statement
The reworking of a service, such as the reprocessing of s lean operation or the replacement of a food order on a restaurant

In short, any cost that would not have been expended if quality were perfect contributes to the cost of quality.

The quality cost represents an important component of sales turnover. According to a brochure prepared by the ‘British Department of Trade and Industry’, the quality cost account for between 5 and 25% of turnover. Nevertheless, one can state that these costs provide an economic means through which company management and those practicing quality control can communicate in business terms effectively.

The history of quality cost concept can be traced back to the early fifties when works entitled “The Economics of Quality” and “The Economic Choice of Sampling System in Acceptance Sampling” was published by Juran and Sittig respectively. Another major milestone in the ‘American Society for Quality Control (ASQC)’ established the “Quality Cost Technical Committee”. Two years later in 1963, the United States Department of Defence issued MIL- Q- 9858A entitled “Quality Problem Requirements”. This document is considered to have played on important role in the development of the quality cost concept as it made “Cost Related to Quality” a Requirement on concerned military contractors and subcontractors.

Ever since then several other developments have taken place and a large number of publication relating to quality costs have appeared.

Categories of Quality costs-

There are four categories of quality costs identified by “American Society of Quality Control” (ASQC)

1) Prevention costs- the cost of activities specifically designed to prevent poor quality on products or service. Example include the costs of new product review., quality planning, supplier capability surveys, process capability evaluations, quality improvement team meetings, quality improvement projects, quality education, and training.

Prevention costs could be misinterpreted on two ways- First, application of the definition of prevention costs could be unclear. Extra appraisal and failure costs may be incurred to prevent more expensive failure costs (for example, added inspections and rework to prevent newly found defects from reaching the customer). These clearly are not prevention costs. But, in the same sense, costs incurred to solve problems (corrective action or failure analysis costs) can be viewed as part of either the problem cost (failure cost) or the cost incurred to prevent the problem on the future (prevention cost). In this case, it does not really matter in which categories the costs are accumulated, as long as there is consistency. The detailed descriptions will attempt to identify elements that might be viewed this way.

The second way in which prevention costs could be misunderstood occurs whenever individual is engaged in prevention activities as an integral but small part of a regular job assignment. On many cases, this may be a highly significant activity, such as control charting by the production operator, and part of the operator’s cost could be considered prevention in the quality cost report. However, some consider this type of prevention activity as a desirable, built-in, self discipline cost that is part of normal operating expenses. This may also include allocations for automated mechanisms, such as a self-checking machine tool, automatic process control equipment, or as inspection edit built into the software for service processing by computer.

On the other hand, individuals, such as engineers or analysis, may work full-time for short periods in activities (such as quality improvements projects) specifically to prevent in the process. this type of activity is clearly intended to be a part of prevention costs.

2) Appraisal Costs- the cost associated with measuring, evaluating, or auditing products or servicer to assure conformance to quality standards and performance requirements. These include the costs of incoming and source inspection / test of purchased material; validation, verification and checking activities; in-process and final inspection / test; product, process, or service audits; calibration of measuring and test; and the cost of associated supplies and material.

3) Failure Costs- the costing from products or services not confirming to requirements or customer / user needs – that is, the cost resulting from poor quality. Failure costs are divided into internal and external failure cost categories.

i) Internal Failure Costs occur prior to delivery or shipment of the product, or the furnishing of a service, to the customer. Examples include costs of scrap, rework, reinsertion, retesting, material review, and downgrading.

ii) External Failure costs occur after delivering or shipment of the product, or during oe after furnishing of a service, to the customer. Examples include the costs of processing customer complaints including necessary field service, customer returns, warranty claims, and products recalls.

Explain the relationship between the process of planning with the agencies / activities

noreply@blogger.com (Manoj Maikhuri) — Sat, 11 Feb 2012 10:54:00 +0000

Process Planning

The objective of the manufacturing process is to transform an idea into a salable product.

The common methodology used to accomplish this objective is to divide the manufacturing process into several activities, arranged serially. Usually, each activity handles a different stage of the process, each stage representing a unique discipline and training.

Fig. 2.1 describes the global structure of an industrial enterprises and points to the role of process planning as a part of production planning. This latter represents the general control of manufacturing activities in an enterprise. Where as the general management of company is concerned with long-range planning activities such as personnel policy, marketing, accounting, etc. it delegates the functions of product design and manufacture to special departments which are in charge of functions such as production planning, fabrication technologies, quality control, etc. the role of process planning itself is now explained.

The place of process planning in the manufacturing cycle-

Process planning is an important link in the manufacturing cycle. It defines in detail the process that transforms raw material into a desired form. The form is defined by the product designer, and is expressed in engineering drawing and GTD- Geometric Dimensioning and Tolerances.

More precisely, process planning can be defined by a sequences of activities illustrate in Fig. 2.2 although not necessarily in the order shown. They comprise mainly.

An interpretation of the specifications contained in the definition drawing of a part, including mainly dimensions and tolerances, geometric tolerances, surface roughness, material type, blank size, number of parts in a batch, etc.;
A selection of processes and tools which are candidates for processing a part and its features by respecting the constraints imposed in the definition drawing;
A determination of production tolerances and setting dimensions which ensure execution of the design tolerances, while choosing production dimensions for reasons of commodity and capability of manufacturing machinery;
A selection of starting surfaces and datum surfaces to ensure precise execution of processing operations simultaneously with a selection of holding fixtures and checking of stability of a part by appropriate damping;
A sequencing of operations as a function of priorities imposed by accuracy and technological constraints;
A grouping of elementary operations on the same machine so that operation time will be reduced, while respecting accuracy requirements;
A selection of machines to execute the technological operations taking into account the number of work pieces to be produced;
A selection of inspection methods and inspection instruments to guarantee final conformity of component with functional requirements;
A determination of processing conditions for every elementary operation which enable the computation of working times and costs in order to carry out an economic evaluation; and
Editing of process sheets to be assembled in a comprehensive process planning file which is transferred to the manufacturing department for, execution.

Relation between Process Planning with other Activities-

The process of detailed planning may commence after product and the technology of processing has been identified. Process of planning involve several activity which are given below in a figure-

As a production and planning I/C of an organisation, what basic factors you will consider while electing a site for a proposed new plant.

noreply@blogger.com (Manoj Maikhuri) — Sat, 10 Dec 2011 11:06:00 +0000

Plant location: Introduction & meaning

Plant location or the facilities location problem is an important strategic level decision-making for an organisation. One of the key features of a conversion process (manufacturing system) is the efficiency with which the products (services) are transferred to the customers. This fact will include the determination of where to pace the plant or facility.

The selection of location is a key-decision as large investment is made in building plant and machinery. It is mot advisable or not possible to change the location very often. So an improper location of plant may lead to waste of all the investments made in building and machinery, equipment.

Before a location for a plant is selected, long range forecast should be made anticipating future needs for the company. The plant location should be based on the company’s expansion plant and policy, diversification plant for the products. Changing market conditions, the changing source of raw material and many other functions of the location study is to find an optimum location on that will result in the greatest advantage to the organisation.

Need for selecting a suitable location-

The need for selecting a suitable location arises because of two situations.

When starting a new organisation, i.e. location choice for the first time
In case of existing organisation

Because of these events, an organisation will be kept in additional or alternate sites for its production activities. So, the plant location becomes as important decision which in turn influences plant layout and facility needed. Also, it influences capital investment and operating costs. For example, in steel industry, if we integrate the units’ right from ore extraction to final steel formation in a nearby area, the transportation cost would be substantially reduced and also, the reliability of supply to the final stages of production in the integrated plant would be improved. This in turn, improves the productivity of the plant.

In case of location choice for the first time or new organisation-

Cost economies are always important while selecting a location for the first time, but should keep in mind the cost of long-term business / organisational objectives. The following are the factors to be considered while selecting the location for the new organisation:

1- Identification of Region- the organisational objectives along with the various long-term consideration about marketing, technology, internal organisational strength and weakness, region- specific resources and business environmental, social environment and geographical environment suggest a suitable region for locating the operations facility.

2- Choice of a site within a region- once the suitable region is identified, the next step is choosing the beat site from an available set. Choice of a site is less dependent on the organisational long term strategies. Evaluation of alternative site for their tangible and intangible costs will resolve facilities- location problem.

The problem of location of a site within the region can be approached with the following cost-oriented non-interactive model, i.e., dimensional analysis.

3- Dimensional Analysis- if all the costs were tangible and quantifiable, the comparison and selection of a site is easy. The location with the least cost is selected. In most of cases intangible costs which are expressed in relative terms than in absolute terms. There relative merits and demerits of sites can also be compared easily. Since both tangible and intangible costs need to be considered for a selection of a site, dimensional analysis is used.

4- Dimensional analysis consists in computing the relative merits (cost ratio) for each of the cost times for two determinative sites. for each of the ratios an appropriate weightange by means of power is given and multiplying these weighted ratio to come with a comprehensive figure on the relative merit of two alternative sites, i.e.,

C1M, C2M, . . . . CzM are the different costs associated with a site M on the ‘z’ different costs items.

C1N, C2N, . . . . CzN are the different costs associated with a site N and W1, W2, W3, . . . . Wz are the weightage given to these cost items, and then relative merit of the M and site N is given by.

If this is >1, sites N is superior and vice-versa.

When starting a new factory, plant location decisions are very important because they have direct bearing on factors like, financial, employment and distribution patterns. In the long run, relocation of plant may be even benefit the organisation. But, the relocation of the plant involves stoppage of production, and also cost for shifting the facilities to a new location. In addition to these things, it will introduce some inconvenience in the normal functioning of the business. Hence, at the time of stating any industry, one should generate several alternate sites for locating the plant. After a critical analysis, the best site is to be selected for commissioning the plant. Location of warehouse and other facilities ate also having direct bearing on the operational performance of organisations.

The existing firms will seek new locations in order to expend the capacity or to place the existing facilities. When the demand for product increases, it will give rise to following decisions:

Whether to expand the existing capacity and facilities
Whether to look for new locations for additional facilities
Whether to close down existing facilities to take advantage of some new location